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Up-to-date 3D building models are important for many applications. Airborne very high resolution (VHR) images often acquired annually give an opportunity to create an up-to-date 3D model. Building segmentation is often the first and utmost step. Convolutional neural networks (CNNs) draw lots of attention in interpreting VHR images as they can learn very effective features for very complex scenes. This paper employs Mask R-CNN to address two problems in building segmentation: detecting different scales of building and segmenting buildings to have accurately segmented edges. Mask R-CNN starts from feature pyramid network (FPN) to create different scales of semantically rich features. FPN is integrated with region proposal network (RPN) to generate objects with various scales with the corresponding optimal scale of features. The features with high and low levels of information are further used for better object classification of small objects and for mask prediction of edges. The method is tested on ISPRS benchmark dataset by comparing results with the fully convolutional networks (FCN), which merge high and low level features by a skip-layer to create a single feature for semantic segmentation. The results show that Mask R-CNN outperforms FCN with around 15% in detecting objects, especially in detecting small objects. Moreover, Mask R-CNN has much better results in edge region than FCN. The results also show that choosing the range of anchor scales in Mask R-CNN is a critical factor in segmenting different scale of objects. This paper provides an insight into how a good anchor scale for different dataset should be chosen. [ABSTRACT FROM AUTHOR]

This paper aims to simulate the application of more advanced computer vision techniques to tracking in biological molecular imaging by surveying the literature and sketching the current state of affairs in the field for a signal and image processing audience. After describing the basic principles of visualizing molecular dynamics in living cells and giving some examples of biological molecular dynamics studies, the paper summarizes the problems and limitations intrinsic to imaging at this scale. The paper then discusses the main ingredients of the commonly used tracking paradigm and subsequently reconsider its competence by comparing it to certain aspects of visual motion perception in human beings, keeping in mind the complexity and variability of biological image data. Finally, it summarizes the main points of attention for future research and the challenges that lie ahead. [ABSTRACT FROM PUBLISHER]

Background: Methods to monitor cardiac functioning non-invasively can accelerate preclinical and clinical research into novel treatment options for heart failure. However, manual image analysis of cardiac substructures is resource-intensive and error-prone. While automated methods exist for clinical CT images, translating these to preclinical μCT data is challenging. We employed deep learning to automate the extraction of quantitative data from both CT and μCT images., Methods: We collected a public dataset of cardiac CT images of human patients, as well as acquired μCT images of wild-type and accelerated aging mice. The left ventricle, myocardium, and right ventricle were manually segmented in the μCT training set. After template-based heart detection, two separate segmentation neural networks were trained using the nnU-Net framework., Results: The mean Dice score of the CT segmentation results (0.925 ± 0.019, n = 40) was superior to those achieved by state-of-the-art algorithms. Automated and manual segmentations of the μCT training set were nearly identical. The estimated median Dice score (0.940) of the test set results was comparable to existing methods. The automated volume metrics were similar to manual expert observations. In aging mice, ejection fractions had significantly decreased, and myocardial volume increased by age 24 weeks., Conclusions: With further optimization, automated data extraction expands the application of (μ)CT imaging, while reducing subjectivity and workload. The proposed method efficiently measures the left and right ventricular ejection fraction and myocardial mass. With uniform translation between image types, cardiac functioning in diastolic and systolic phases can be monitored in both animals and humans., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

BRCA2 is an essential tumor suppressor protein involved in promoting faithful repair of DNA lesions. The activity of BRCA2 needs to be tuned precisely to be active when and where it is needed. Here, we quantified the spatio-temporal dynamics of BRCA2 in living cells using aberration-corrected multifocal microscopy (acMFM). Using multicolor imaging to identify DNA damage sites, we were able to quantify its dynamic motion patterns in the nucleus and at DNA damage sites. While a large fraction of BRCA2 molecules localized near DNA damage sites appear immobile, an additional fraction of molecules exhibits subdiffusive motion, providing a potential mechanism to retain an increased number of molecules at DNA lesions. Super-resolution microscopy revealed inhomogeneous localization of BRCA2 relative to other DNA repair factors at sites of DNA damage. This suggests the presence of multiple nanoscale compartments in the chromatin surrounding the DNA lesion, which could play an important role in the contribution of BRCA2 to the regulation of the repair process., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Purpose: Our aim is to automatically align digital subtraction angiography (DSA) series, recorded before and after endovascular thrombectomy. Such alignment may enable quantification of procedural success., Methods: Firstly, we examine the inherent limitations for image registration, caused by the projective characteristics of DSA imaging, in a representative set of image pairs from thrombectomy procedures. Secondly, we develop and assess various image registration methods (SIFT, ORB). We assess these methods using manually annotated point correspondences for thrombectomy image pairs., Results: Linear transformations that account for scale differences are effective in aligning DSA sequences. Two anatomical landmarks can be reliably identified for registration using a U-net. Point-based registration using SIFT and ORB proves to be most effective for DSA registration and are applicable to recordings for all patient sub-types. Image-based techniques are less effective and did not refine the results of the best point-based registration method., Conclusion: We developed and assessed an automated image registration approach for cerebral DSA sequences, recorded before and after endovascular thrombectomy. Accurate results were obtained for approximately 85% of our image pairs., (© 2023. The Author(s).)

A wide range of nuclear proteins are involved in the spatio-temporal organization of the genome through diverse biological processes such as gene transcription and DNA replication. Upon stimulation by testosterone and translocation to the nucleus, multiple androgen receptors (ARs) accumulate in microscopically discernable foci which are irregularly distributed in the nucleus. Here, we investigated the formation and physical nature of these foci, by combining novel fluorescent labeling techniques to visualize a defined chromatin locus of AR-regulated genes-PTPRN2 or BANP-simultaneously with either AR foci or individual AR molecules. Quantitative colocalization analysis showed evidence of AR foci formation induced by R1881 at both PTPRN2 and BANP loci. Furthermore, single-particle tracking (SPT) revealed three distinct subdiffusive fractional Brownian motion (fBm) states: immobilized ARs were observed near the labeled genes likely as a consequence of DNA-binding, while the intermediate confined state showed a similar spatial behavior but with larger displacements, suggesting compartmentalization by liquid-liquid phase separation (LLPS), while freely mobile ARs were diffusing in the nuclear environment. All together, we show for the first time in living cells the presence of AR-regulated genes in AR foci., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Hematopoietic stem cells (HSCs) express a large variety of cell surface receptors that are associated with acquisition of self-renewal and multipotent properties. Correct expression of these receptors depends on a delicate balance between cell surface trafficking, recycling, and degradation and is controlled by the microtubule network and Golgi apparatus, whose roles have hardly been explored during embryonic/fetal hematopoiesis. Here we show that, in the absence of CLASP2, a microtubule-associated protein, the overall production of HSCs is reduced, and the produced HSCs fail to self-renew and maintain their stemness throughout mouse and zebrafish development. This phenotype can be attributed to decreased cell surface expression of the hematopoietic receptor c-Kit, which originates from increased lysosomal degradation in combination with a reduction in trafficking to the plasma membrane. A dysfunctional Golgi apparatus in CLASP2-deficient HSCs seems to be the underlying cause of the c-Kit expression and signaling imbalance., Competing Interests: Declaration of interests The authors declare no competing financial interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers., (© 2021. The Author(s).)

Protein phosphatase 2B (PP2B) is critical for synaptic plasticity and learning, but the molecular mechanisms involved remain unclear. Here we identified different types of proteins that interact with PP2B, including various structural proteins of the postsynaptic densities (PSDs) of Purkinje cells (PCs) in mice. Deleting PP2B reduced expression of PSD proteins and the relative thickness of PSD at the parallel fiber to PC synapses, whereas reexpression of inactive PP2B partly restored the impaired distribution of nanoclusters of PSD proteins, together indicating a structural role of PP2B. In contrast, lateral mobility of surface glutamate receptors solely depended on PP2B phosphatase activity. Finally, the level of motor learning covaried with both the enzymatic and nonenzymatic functions of PP2B. Thus, PP2B controls synaptic function and learning both through its action as a phosphatase and as a structural protein that facilitates synapse integrity. SIGNIFICANCE STATEMENT Phosphatases are generally considered to serve their critical role in learning and memory through their enzymatic operations. Here, we show that protein phosphatase 2B (PP2B) interacts with structural proteins at the synapses of cerebellar Purkinje cells. Differentially manipulating the enzymatic and structural domains of PP2B leads to different phenotypes in cerebellar learning. We propose that PP2B is crucial for cerebellar learning via two complementary actions, an enzymatic and a structural operation., (Copyright © 2021 Lin et al.)

The stalled fork protection pathway mediated by breast cancer 1/2 (BRCA1/2) proteins is critical for replication fork stability. However, it is unclear whether additional mechanisms are required to maintain replication fork stability. We describe a hitherto unknown mechanism, by which the SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily-A containing DEAD/H box-1 (SMARCAD1) stabilizes active replication forks, that is essential to maintaining resistance towards replication poisons. We find that SMARCAD1 prevents accumulation of 53BP1-associated nucleosomes to preclude toxic enrichment of 53BP1 at the forks. In the absence of SMARCAD1, 53BP1 mediates untimely dissociation of PCNA via the PCNA-unloader ATAD5, causing frequent fork stalling, inefficient fork restart, and accumulation of single-stranded DNA. Although loss of 53BP1 in SMARCAD1 mutants rescues these defects and restores genome stability, this rescued stabilization also requires BRCA1-mediated fork protection. Notably, fork protection-challenged BRCA1-deficient naïve- or chemoresistant tumors require SMARCAD1-mediated active fork stabilization to maintain unperturbed fork progression and cellular proliferation., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Motivation: Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data., Results: Here, we present a deep-learning-based method for the data association stage of particle tracking. The proposed method uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next. Comprehensive evaluations on datasets from the particle tracking challenge demonstrate the competitiveness of the proposed deep-learning method compared to the state of the art. Additional tests on real-time-lapse fluorescence microscopy images of various types of intracellular particles show the method performs comparably with human experts., Availability and Implementation: The software code implementing the proposed method as well as a description of how to obtain the test data used in the presented experiments will be available for non-commercial purposes from https://github.com/yoyohoho0221/pt_linking., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Intracellular transport relies on multiple kinesins, but it is poorly understood which kinesins are present on particular cargos, what their contributions are and whether they act simultaneously on the same cargo. Here, we show that Rab6-positive secretory vesicles are transported from the Golgi apparatus to the cell periphery by kinesin-1 KIF5B and kinesin-3 KIF13B, which determine the location of secretion events. KIF5B plays a dominant role, whereas KIF13B helps Rab6 vesicles to reach freshly polymerized microtubule ends, to which KIF5B binds poorly, likely because its cofactors, MAP7-family proteins, are slow in populating these ends. Sub-pixel localization demonstrated that during microtubule plus-end directed transport, both kinesins localize to the vesicle front and can be engaged on the same vesicle. When vesicles reverse direction, KIF13B relocates to the middle of the vesicle, while KIF5B shifts to the back, suggesting that KIF5B but not KIF13B undergoes a tug-of-war with a minus-end directed motor., Competing Interests: AS, MM, EK, IG, CP, QL, PH, YY, VS, IS, EM, LK No competing interests declared, LP Reviewing editor, eLife, AA Deputy editor, eLife, (© 2020, Serra-Marques et al.)

Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the risk of kidney failure. When using fluoroscopic images, the interventional cardiologist needs to rely on a mental anatomical reconstruction. This paper reports on the development of a novel dynamic coronary roadmapping approach for improving visual feedback and reducing contrast use during PCI. The approach compensates cardiac and respiratory induced vessel motion by ECG alignment and catheter tip tracking in X-ray fluoroscopy, respectively. In particular, for accurate and robust tracking of the catheter tip, we proposed a new deep learning based Bayesian filtering method that integrates the detection outcome of a convolutional neural network and the motion estimation between frames using a particle filtering framework. The proposed roadmapping and tracking approaches were validated on clinical X-ray images, achieving accurate performance on both catheter tip tracking and dynamic coronary roadmapping experiments. In addition, our approach runs in real-time on a computer with a single GPU and has the potential to be integrated into the clinical workflow of PCI procedures, providing cardiologists with visual guidance during interventions without the need of extra use of contrast agent., (Copyright © 2020. Published by Elsevier B.V.)

Quantitative analysis of dynamic processes in living cells using time-lapse microscopy requires not only accurate tracking of every particle in the images, but also reliable extraction of biologically relevant parameters from the resulting trajectories. Whereas many methods exist to perform the tracking task, there is still a lack of robust solutions for subsequent parameter extraction and analysis. Here a novel method is presented to address this need. It uses for the first time a deep learning approach to segment single particle trajectories into consistent tracklets (trajectory segments that exhibit one type of motion) and then performs moment scaling spectrum analysis of the tracklets to estimate the number of mobility classes and their associated parameters, providing rich fundamental knowledge about the behavior of the particles under study. Experiments on in-house datasets as well as publicly available particle tracking data for a wide range of proteins with different dynamic behavior demonstrate the broad applicability of the method.

The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screening and analysis. The first critical step toward fully automated high-content image analyses in such studies is to detect all neuronal cells and distinguish them from possible non-neuronal cells or artifacts in the images. Here we investigate the performance of well-established machine learning techniques for this purpose. These include support vector machines, random forests, k-nearest neighbors, and generalized linear model classifiers, operating on an extensive set of image features extracted using the compound hierarchy of algorithms representing morphology, and the scale-invariant feature transform. We present experiments on a dataset of rat hippocampal neurons from our own studies to find the most suitable classifier(s) and subset(s) of features in the common practical setting where there is very limited annotated data for training. The results indicate that a random forests classifier using the right feature subset ranks best for the considered task, although its performance is not statistically significantly better than some support vector machine based classification models.

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.

End-binding proteins (EBs) are the core components of microtubule plus end tracking protein complexes, but it is currently unknown whether they are essential for mammalian microtubule organization. Here, by using CRISPR/Cas9-mediated knockout technology, we generated stable cell lines lacking EB2 and EB3 and the C-terminal partner-binding half of EB1. These cell lines show only mild defects in cell division and microtubule polymerization. However, the length of CAMSAP2-decorated stretches at noncentrosomal microtubule minus ends in these cells is reduced, microtubules are detached from Golgi membranes, and the Golgi complex is more compact. Coorganization of microtubules and Golgi membranes depends on the EB1/EB3-myomegalin complex, which acts as membrane-microtubule tether and counteracts tight clustering of individual Golgi stacks. Disruption of EB1 and EB3 also perturbs cell migration, polarity, and the distribution of focal adhesions. EB1 and EB3 thus affect multiple interphase processes and have a major impact on microtubule minus end organization., (© 2017 Yang et al.)

In minimal invasive image guided catheterization procedures, physicians require information of the catheter position with respect to the patient's vasculature. However, in fluoroscopic images, visualization of the vasculature requires toxic contrast agent. Static vasculature roadmapping, which can reduce the usage of iodine contrast, is hampered by the breathing motion in abdominal catheterization. In this paper, we propose a method to track the catheter tip inside the patient's 3D vessel tree using intra-operative single-plane 2D X-ray image sequences and a peri-operative 3D rotational angiography (3DRA). The method is based on a hidden Markov model (HMM) where states of the model are the possible positions of the catheter tip inside the 3D vessel tree. The transitions from state to state model the probabilities for the catheter tip to move from one position to another. The HMM is updated following the observation scores, based on the registration between the 2D catheter centerline extracted from the 2D X-ray image, and the 2D projection of 3D vessel tree centerline extracted from the 3DRA. The method is extensively evaluated on simulated and clinical datasets acquired during liver abdominal catheterization. The evaluations show a median 3D tip tracking error of 2.3 mm with optimal settings in simulated data. The registered vessels close to the tip have a median distance error of 4.7 mm with angiographic data and optimal settings. Such accuracy is sufficient to help the physicians with an up-to-date roadmapping. The method tracks in real-time the catheter tip and enables roadmapping during catheterization procedures.

The study of intracellular dynamic processes is of fundamental importance for understanding a wide variety of diseases and developing effective drugs and therapies. Advanced fluorescence microscopy imaging systems nowadays allow the recording of virtually any type of process in space and time with super-resolved detail and with high sensitivity and specificity. The large volume and high information content of the resulting image data, and the desire to obtain objective, quantitative descriptions and biophysical models of the processes of interest, require a high level of automation in data analysis. Two key tasks in extracting biologically meaningful information about intracellular dynamics from image data are particle tracking and particle trajectory analysis. Here we present state-of-the-art software tools for these tasks and describe how to use them.

Image registration is typically formulated as an optimization process, which aims to find the optimal transformation parameters of a given transformation model by minimizing a cost function. Local minima may exist in the optimization landscape, which could hamper the optimization process. To eliminate local minima, smoothing the cost function would be desirable. In this paper, we investigate the use of a randomized smoothing (RS) technique for stochastic gradient descent (SGD) optimization, to effectively smooth the cost function. In this approach, Gaussian noise is added to the transformation parameters prior to computing the cost function gradient in each iteration of the SGD optimizer. The approach is suitable for both rigid and nonrigid registrations. Experiments on synthetic images, cell images, public CT lung data, and public MR brain data demonstrate the effectiveness of the novel RS technique in terms of registration accuracy and robustness., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The microtubule (MT) cytoskeleton forms a dynamic filamentous network that is essential for many processes, including mitosis, cell polarity and shape, neurite outgrowth and migration, and ciliogenesis [1, 2]. MTs are built up of α/β-tubulin heterodimers, and their dynamic behavior is in part regulated by tubulin-associated proteins (TAPs). Here we describe a novel system to study mammalian tubulins and TAPs. We co-expressed equimolar amounts of triple-tagged α-tubulin and β-tubulin using a 2A "self-cleaving" peptide and isolated functional fluorescent tubulin dimers from transfected HEK293T cells with a rapid two-step approach. We also produced two mutant tubulins that cause brain malformations in tubulinopathy patients [3]. We then applied a paired mass-spectrometry-based method to identify tubulin-binding proteins in HEK293T cells and describe both novel and known TAPs. We find that CKAP5 and the CLASPs, which are MT plus-end-tracking proteins with TOG(L)-domains [4], bind tubulin efficiently, as does the Golgi-associated protein GCC185, which interacts with the CLASPs [5]. The N-terminal TOGL domain of CLASP1 contributes to tubulin binding and allows CLASP1 to function as an autonomous MT-growth-promoting factor. Interestingly, mutant tubulins bind less well to a number of TAPs, including CLASPs and GCC185, and incorporate less efficiently into cellular MTs. Moreover, expression of these mutants in cells impairs several MT-growth-related processes involving TAPs. Thus, stable tubulin-TAP interactions regulate MT nucleation and growth in cells. Combined, our results provide a resource for investigating tubulin interactions and functions and widen the spectrum of tubulin-related disease mechanisms., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.

Biological studies of intracellular dynamic processes commonly require motion analysis of large numbers of particles in live-cell time-lapse fluorescence microscopy imaging data. Many particle tracking methods have been developed in the past years as a first step toward fully automating this task and enabling high-throughput data processing. Two crucial aspects of any particle tracking method are the detection of relevant particles in the image frames and their linking or association from frame to frame to reconstruct the trajectories. The performance of detection techniques as well as specific combinations of detection and linking techniques for particle tracking have been extensively evaluated in recent studies. Comprehensive evaluations of linking techniques per se, on the other hand, are lacking in the literature. Here we present the results of a quantitative comparison of data association techniques for solving the linking problem in biological particle tracking applications. Nine multiframe and two more traditional two-frame techniques are evaluated as a function of the level of missing and spurious detections in various scenarios. The results indicate that linking techniques are generally more negatively affected by missing detections than by spurious detections. If misdetections can be avoided, there appears to be no need to use sophisticated multiframe linking techniques. However, in the practically likely case of imperfect detections, the latter are a safer choice. Our study provides users and developers with novel information to select the right linking technique for their applications, given a detection technique of known quality., (Copyright © 2015 Elsevier B.V. All rights reserved.)

A hallmark of the neuromuscular junction (NMJ) is the high density of acetylcholine receptors (AChRs) in the postsynaptic muscle membrane. The postsynaptic apparatus of the NMJ is organized by agrin secreted from motor neurons. The mechanisms that underlie the focal delivery of AChRs to the adult NMJ are not yet understood in detail. We previously showed that microtubule (MT) capture by the plus end-tracking protein CLASP2 regulates AChR density at agrin-induced AChR clusters in cultured myotubes via PI3 kinase acting through GSK3β. Here we show that knockdown of the CLASP2-interaction partner LL5β by RNAi and forced expression of a CLASP2 fragment blocking the CLASP2/LL5β interaction inhibit microtubule capture. The same treatments impair focal vesicle delivery to the clusters. Consistent with these findings, knockdown of LL5β at the NMJ in vivo reduces the density and insertion of AChRs into the postsynaptic membrane. MT capture and focal vesicle delivery to agrin-induced AChR clusters are also inhibited by microtubule- and actin-depolymerizing drugs, invoking both cytoskeletal systems in MT capture and in the fusion of AChR vesicles with the cluster membrane. Combined our data identify a transport system, organized by agrin through PI3 kinase, GSK3β, CLASP2, and LL5β, for precise delivery of AChR vesicles from the subsynaptic nuclei to the overlying synaptic membrane., (© 2015 Basu et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).)

Cell migration is crucial in development, tissue repair and immunity and frequently aberrant in pathological processes including tumor metastasis. Focal adhesions (FAs) are integrin-based adhesion complexes that form the link between the cytoskeleton and the extracellular matrix and are thought to orchestrate cell migration. Understanding the regulation of FAs by (oncogenic) signaling pathways may identify strategies to target pathological cell migration. Here we describe the development of a robust FA tracker that enables the automatic, multi-parametric analysis of FA dynamics, morphology and composition from time-lapse image series generated by total internal reflection fluorescence (TIRF) microscopy. In control prostate carcinoma cells, this software recapitulates previous findings that relate morphological characteristics of FAs to their lifetime and their cellular location. We then investigated how FAs are altered when cell migration is induced by the metastasis-promoting hormone HGF and subsequently inhibited by activation of the small GTPase Rap1. We performed a detailed analysis of individual FA parameters, which identified FA size, sliding and intensity as primary targets of Rap1. HGF did not have strong effects on any of the FA parameters within the first hours of its addition. Subsequent Bayesian network inference (BNI), using all measured parameters as input, revealed little correlation between changes in cell migration and FA characteristics in this prostate carcinoma cell line. Instead BNI indicated a concerted coordination of cell size and FA parameters. Thus our results did not reveal a direct relation between the regulation of cell migration and the regulation of FA dynamics., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Genome maintenance by homologous recombination depends on coordinating many proteins in time and space to assemble at DNA break sites. To understand this process, we followed the mobility of BRCA2, a critical recombination mediator, in live cells at the single-molecule level using both single-particle tracking and fluorescence correlation spectroscopy. BRCA2-GFP and -YFP were compared to distinguish diffusion from fluorophore behavior. Diffusive behavior of fluorescent RAD51 and RAD54 was determined for comparison. All fluorescent proteins were expressed from endogenous loci. We found that nuclear BRCA2 existed in oligomeric clusters, and exhibited heterogeneous mobility. DNA damage increased BRCA2 transient binding, presumably including binding to damaged sites. Despite its very different size, RAD51 displayed mobility similar to BRCA2, which indicates physical interaction between these proteins both before and after induction of DNA damage. We propose that BRCA2-mediated sequestration of nuclear RAD51 serves to prevent inappropriate DNA interactions and that all RAD51 is delivered to DNA damage sites in association with BRCA2., (© 2014 Reuter et al.)

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Microtubule-targeting agents (MTAs) are widely used for treatment of cancer and other diseases, and a detailed understanding of the mechanism of their action is important for the development of improved microtubule-directed therapies. Although there is a large body of data on the interactions of different MTAs with purified tubulin and microtubules, much less is known about how the effects of MTAs are modulated by microtubule-associated proteins. Among the regulatory factors with a potential to have a strong impact on MTA activity are the microtubule plus end-tracking proteins, which control multiple aspects of microtubule dynamic instability. Here, we reconstituted microtubule dynamics in vitro to investigate the influence of end-binding proteins (EBs), the core components of the microtubule plus end-tracking protein machinery, on the effects that MTAs exert on microtubule plus-end growth. We found that EBs promote microtubule catastrophe induction in the presence of all MTAs tested. Analysis of microtubule growth times supported the view that catastrophes are microtubule age dependent. This analysis indicated that MTAs affect microtubule aging in multiple ways: destabilizing MTAs, such as colchicine and vinblastine, accelerate aging in an EB-dependent manner, whereas stabilizing MTAs, such as paclitaxel and peloruside A, induce not only catastrophes but also rescues and can reverse the aging process.

Achieving complete understanding of any living thing inevitably requires thorough analysis of both its anatomic and dynamic properties. Live-cell imaging experiments carried out to this end often produce massive amounts of time-lapse image data containing far more information than can be digested by a human observer. Computerized image analysis offers the potential to take full advantage of available data in an efficient and reproducible manner. A recurring task in many experiments is the tracking of large numbers of cells or particles and the analysis of their (morpho)dynamic behavior. In the past decade, many methods have been developed for this purpose, and software tools based on these are increasingly becoming available. Here, we survey the latest developments in this area and discuss the various computational approaches, software tools, and quantitative measures for tracking and motion analysis of cells and particles in time-lapse microscopy images., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method for studying regional heart dynamics. It offers great potential for quantitative analysis of a variety of kine(ma)tic parameters, but its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we evaluate the performance of four frequently used concepts found in the literature (optical flow, harmonic phase (HARP) magnetic resonance imaging, active contour fitting, and non-rigid image registration) for cardiac motion analysis in 2D tMRI image sequences, using both synthetic image data (with ground truth) and real data from preclinical (small animal) and clinical (human) studies. In addition we propose a new probabilistic method for tag tracking that serves as a complementary step to existing methods. The new method is based on a Bayesian estimation framework, implemented by means of reversible jump Markov chain Monte Carlo (MCMC) methods, and combines information about the heart dynamics, the imaging process, and tag appearance. The experimental results demonstrate that the new method improves the performance of even the best of the four previous methods. Yielding higher consistency, accuracy, and intrinsic tag reliability assessment, the proposed method allows for improved analysis of cardiac motion., (Copyright © 2011 Elsevier B.V. All rights reserved.)

End-binding proteins (EBs) comprise a conserved family of microtubule plus end-tracking proteins. The concerted action of calponin homology (CH), linker, and C-terminal domains of EBs is important for their autonomous microtubule tip tracking, regulation of microtubule dynamics, and recruitment of numerous partners to microtubule ends. Here we report the detailed structural and biochemical analysis of mammalian EBs. Small-angle X-ray scattering, electron microscopy, and chemical cross-linking in combination with mass spectrometry indicate that EBs are elongated molecules with two interacting CH domains, an arrangement reminiscent of that seen in other microtubule- and actin-binding proteins. Removal of the negatively charged C-terminal tail did not affect the overall conformation of EBs; however, it increased the dwell times of EBs on the microtubule lattice in microtubule tip-tracking reconstitution experiments. An even more stable association with the microtubule lattice was observed when the entire negatively charged C-terminal domain of EBs was replaced by a neutral coiled-coil motif. In contrast, the interaction of EBs with growing microtubule tips was not significantly affected by these C-terminal domain mutations. Our data indicate that long-range electrostatic repulsive interactions between the C-terminus and the microtubule lattice drive the specificity of EBs for growing microtubule ends.

Rab6 is a conserved small GTPase that localizes to the Golgi apparatus and cytoplasmic vesicles and controls transport and fusion of secretory carriers [1]. Another Rab implicated in trafficking from the trans-Golgi to the plasma membrane is Rab8 [2-5]. Here we show that Rab8A stably associates with exocytotic vesicles in a Rab6-dependent manner. Rab8A function is not needed for budding or motility of exocytotic carriers but is required for their docking and fusion. These processes also depend on the Rab6-interacting cortical factor ELKS [1], suggesting that Rab8A and ELKS act in the same pathway. We show that Rab8A and ELKS can be linked by MICAL3, a member of the MICAL family of flavoprotein monooxygenases [6]. Expression of a MICAL3 mutant with an inactive monooxygenase domain resulted in a strong accumulation of secretory vesicles that were docked at the cell cortex but failed to fuse with the plasma membrane, an effect that correlated with the strongly reduced mobility of MICAL3. We propose that the monooxygenase activity of MICAL3 is required to regulate its own turnover and the concomitant remodeling of vesicle-docking protein complexes in which it is engaged. Taken together, the results of our study illustrate cooperation of two Rab proteins in constitutive exocytosis and implicates a redox enzyme in this process., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method allowing quantitative analysis of regional heart dynamics. Its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we propose a novel probabilistic method for tag tracking, implemented by means of Bayesian particle filtering and a trans-dimensional Markov chain Monte Carlo (MCMC) approach, which efficiently combines information about the imaging process and tag appearance with prior knowledge about the heart dynamics obtained by means of non-rigid image registration. Experiments using synthetic image data (with ground truth) and real data (with expert manual annotation) from preclinical (small animal) and clinical (human) studies confirm that the proposed method yields higher consistency, accuracy, and intrinsic tag reliability assessment in comparison with other frequently used tag tracking methods.

The kinesin-13 family member mitotic centromere-associated kinesin (MCAK) is a potent microtubule depolymerase. Paradoxically, in cells it accumulates at the growing, rather than the shortening, microtubule plus ends. This plus-end tracking behavior requires the interaction between MCAK and members of the end-binding protein (EB) family, but the effect of EBs on the microtubule-destabilizing activity of MCAK and the functional significance of MCAK accumulation at the growing microtubule tips have so far remained elusive. Here, we dissect the functional interplay between MCAK and EB3 by reconstituting EB3-dependent MCAK activity on dynamic microtubules in vitro. Whereas MCAK alone efficiently blocks microtubule assembly, the addition of EB3 restores robust microtubule growth, an effect that is not dependent on the binding of MCAK to EB3. At the same time, EB3 targets MCAK to growing microtubule ends by increasing its association rate with microtubule tips, a process that requires direct interaction between the two proteins. This EB3-dependent microtubule plus-end accumulation does not affect the velocity of microtubule growth or shortening but enhances the capacity of MCAK to induce catastrophes. The combination of MCAK and EB3 thus promotes rapid switching between microtubule growth and shortening, which can be important for remodeling of the microtubule cytoskeleton., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

In Chinese hamster ovary cells, microtubules originate at the microtubule organizing center (MTOC) and grow persistently toward the cell edge, where they undergo catastrophe. In axons, microtubule dynamics must be regulated differently because microtubules grow parallel to the plasma membrane and there is no MTOC. GFP-tagged microtubule plus end tracking proteins (+TIPs) mark the ends of growing neuronal microtubules. Their fluorescent "comet-like" pattern reflects turnover of +TIP binding sites. Using GFP-tagged +TIPs and fluorescence-based segmentation and tracking tools, we show that axonal microtubules grow with a constant average velocity and that they undergo catastrophes at random positions, yet in a programmed fashion. Using protein depletion approaches, we find that the +TIPs CLIP-115 and CLIP-170 affect average microtubule growth rate and growth distance in neurons but not the duration of a microtubule growth event. In N1E-115 neuroblastoma cells, we find that EB1, the core +TIP, regulates microtubule growth rate, growth distance, and duration, consistent with in vitro data. Combined, our data suggest that CLIPs influence the axonal microtubule/tubulin ratio, whereas EB1 stimulates microtubule growth and structural transitions at microtubule ends, thereby regulating microtubule catastrophes and the turnover of +TIP binding sites., (2010 Elsevier Ltd. All rights reserved.)

The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.

Studying intracellular dynamics is of major importance for understanding healthy life at the molecular level and for developing drugs to target disease processes. One of the key technologies to enable this research is the automated tracking and motion analysis of subcellular objects in microscopy image sequences. Contrary to common frame-by-frame tracking methods, two alternative approaches have been proposed recently, based on either Bayesian estimation or space-time segmentation, which better exploit the available spatiotemporal information. In this paper, we propose to combine the power of both approaches, and develop a new probabilistic method to segment the traces of the moving objects in kymograph representations of the image data. It is based on variable-rate particle filtering and uses multiscale trend analysis for estimation of the relevant kinematic parameters using the extracted traces. Experiments on realistic synthetically generated images as well as on real biological image data demonstrate the improved potential of the new method for the analysis of microtubule dynamics in vitro.