3D actin network centerline extraction with multiple active contours

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics.

Tracing and tracking filamentous structures across scales: A systematic review

Filamentous structures are ubiquitous in nature, are studied in diverse scientific fields, and span vastly different spatial scales. Filamentous structures in biological systems fulfill different functions and often form dynamic networks that respond to perturbations. Therefore, characterizing the properties of filamentous structures and the networks they form is important to gain better understanding of systems level functions and dynamics. Filamentous structures are captured by various imaging technologies, and analysis of the resulting imaging data addresses two problems: (i) identification (tracing) of filamentous structures in a single snapshot and (ii) characterizing the dynamics (i.e., tracking) of filamentous structures over time. Therefore, considerable research efforts have been made in developing automated methods for tracing and tracking of filamentous structures. Here, we provide a systematic review in which we present, categorize, and discuss the state-of-the-art methods for tracing and tracking of filamentous structures in sparse and dense networks. We highlight the mathematical approaches, assumptions, and constraints particular for each method, allowing us to pinpoint outstanding challenges and offer perspectives for future research aimed at gaining better understanding of filamentous structures in biological systems.

Label-free monitoring of spatiotemporal changes in the stem cell cytoskeletons in time-lapse phase-contrast microscopy

Investigation of the dynamic structural changes in the actin cytoskeleton during cell migration provides crucial information about the physiological conditions of a stem cell during in-vitro culture. Here we proposed a quantitative analytical model associated with texture extraction with cell tracking techniques for in situ monitoring of the cytoskeletal density change of stem cells in phase-contrast microscopy without fluorescence staining. The reliability of the model in quantifying the texture density with different orientation was first validated using a series of simulated textural images. The capability of the method to reflect the spatiotemporal regulation of the cytoskeletal structure of a living stem cell was further proved by applying it to a set of 72 h phase-contrast microscopic video of the growth dynamics of mesenchymal stem cells in vitro culture.

Actin crosslinker competition and sorting drive emergent GUV size-dependent actin network architecture

The proteins that make up the actin cytoskeleton can self-assemble into a variety of structures. In vitro experiments and coarse-grained simulations have shown that the actin crosslinking proteins α-actinin and fascin segregate into distinct domains in single actin bundles with a molecular size-dependent competition-based mechanism. Here, by encapsulating actin, α-actinin, and fascin in giant unilamellar vesicles (GUVs), we show that physical confinement can cause these proteins to form much more complex structures, including rings and asters at GUV peripheries and centers; the prevalence of different structures depends on GUV size. Strikingly, we found that α-actinin and fascin self-sort into separate domains in the aster structures with actin bundles whose apparent stiffness depends on the ratio of the relative concentrations of α-actinin and fascin. The observed boundary-imposed effect on protein sorting may be a general mechanism for creating emergent structures in biopolymer networks with multiple crosslinkers.

Actin crosslinker competition and sorting drive emergent GUV size-dependent actin network architecture

The proteins that make up the actin cytoskeleton can self-assemble into a variety of structures. In vitro experiments and coarse-grained simulations have shown that the actin crosslinking proteins α-actinin and fascin segregate into distinct domains in single actin bundles with a molecular size-dependent competition-based mechanism. Here, by encapsulating actin, α-actinin, and fascin in giant unilamellar vesicles (GUVs), we show that physical confinement can cause these proteins to form much more complex structures, including rings and asters at GUV peripheries and centers; the prevalence of different structures depends on GUV size. Strikingly, we found that α-actinin and fascin self-sort into separate domains in the aster structures with actin bundles whose apparent stiffness depends on the ratio of the relative concentrations of α-actinin and fascin. The observed boundary-imposed effect on protein sorting may be a general mechanism for creating emergent structures in biopolymer networks with multiple crosslinkers.

By encapsulating proteins in giant unilamellar vesicles, Bashirzadeh et al find that actin crosslinkers, α-actinin and fascin, can self-assemble with actin into complex structures that depend on the degree of confinement. Further analysis and modeling show that α-actinin and fascin sort to separate domains of these structures. These insights may be generalizable to other biopolymer networks containing crosslinkers.

Oblique circle method for measuring the curvature and twist of mitotic spindle microtubule bundles

The highly ordered spatial organization of microtubule bundles in the mitotic spindle is crucial for its proper functioning. The recent discovery of twisted shapes of microtubule bundles and spindle chirality suggests that the bundles extend along curved paths in three dimensions, rather than being confined to a plane. This, in turn, implies that rotational forces, i.e., torques, exist in the spindle in addition to the widely studied linear forces. However, studies of spindle architecture and forces are impeded by a lack of a robust method for the geometric quantification of microtubule bundles in the spindle. In this work, we describe a simple method for measuring and evaluating the shapes of microtubule bundles by characterizing them in terms of their curvature and twist. By using confocal microscopy, we obtain three-dimensional images of spindles, which allows us to trace the entire microtubule bundle. For each traced bundle, we first fit a plane and then fit a circle lying in that plane. With this robust method, we extract the curvature and twist, which represent the geometric information characteristic for each bundle. As the bundle shapes reflect the forces within them, this method is valuable for the understanding of forces that act on chromosomes during mitosis.

Network organisation and the dynamics of tubules in the endoplasmic reticulum

The endoplasmic reticulum (ER) is a eukaryotic subcellular organelle composed of tubules and sheet-like areas of membrane connected at junctions. The tubule network is highly dynamic and undergoes rapid and continual rearrangement. There are currently few tools to evaluate network organisation and dynamics. We quantified ER network organisation in Vero and MRC5 cells, and developed an analysis workflow for dynamics of established tubules in live cells. The persistence length, tubule length, junction coordination number and angles of the network were quantified. Hallmarks of imbalances in ER tension, indications of interactions with microtubules and other subcellular organelles, and active dynamics were observed. Clear differences in dynamic behaviour were observed for established tubules at different positions within the cell using itemset mining. We found that tubules with activity-driven fluctuations were more likely to be located away from the cell periphery and a population of peripheral tubules with no signs of active motion was found.

Modelling Motility: The Mathematics of Spermatozoa

In one of the first examples of how mechanics can inform axonemal mechanism, Machin's study in the 1950s highlighted that observations of sperm motility cannot be explained by molecular motors in the cell membrane, but would instead require motors distributed along the flagellum. Ever since, mechanics and hydrodynamics have been recognised as important in explaining the dynamics, regulation, and guidance of sperm. More recently, the digitisation of sperm videomicroscopy, coupled with numerous modelling and methodological advances, has been bringing forth a new era of scientific discovery in this field. In this review, we survey these advances before highlighting the opportunities that have been generated for both recent research and the development of further open questions, in terms of the detailed characterisation of the sperm flagellum beat and its mechanics, together with the associated impact on cell behaviour. In particular, diverse examples are explored within this theme, ranging from how collective behaviours emerge from individual cell responses, including how these responses are impacted by the local microenvironment, to the integration of separate advances in the fields of flagellar analysis and flagellar mechanics.

Fascin-induced actin protrusions are suppressed by dendritic networks in giant unilamellar vesicles

The interactions between actin networks and cell membrane are immensely important for eukaryotic cell functions including cell shape changes, motility, polarity establishment, and adhesion. Actin-binding proteins are known to compete and cooperate using a finite amount of actin monomers to form distinct actin networks. How actin-bundling protein fascin and actin-branching protein Arp2/3 complex compete to remodel membranes is not entirely clear. To investigate fascin- and Arp2/3-mediated actin network remodeling, we applied a reconstitution approach encapsulating bundled and dendritic actin networks inside giant unilamellar vesicles (GUVs). Independently reconstituted, membrane-bound Arp2/3 nucleation forms an actin cortex in GUVs, whereas fascin mediates formation of actin bundles that protrude out of GUVs. Coencapsulating both fascin and Arp2/3 complex leads to polarized dendritic aggregates and significantly reduces membrane protrusions, irrespective of whether the dendritic network is membrane bound or not. However, reducing Arp2/3 complex while increasing fascin restores membrane protrusion. Such changes in network assembly and the subsequent interplay with membrane can be attributed to competition between fascin and Arp2/3 complex to utilize a finite pool of actin.

Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review

Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.

Reconstitution of contractile actomyosin rings in vesicles

One of the grand challenges of bottom-up synthetic biology is the development of minimal machineries for cell division. The mechanical transformation of large-scale compartments, such as Giant Unilamellar Vesicles (GUVs), requires the geometry-specific coordination of active elements, several orders of magnitude larger than the molecular scale. Of all cytoskeletal structures, large-scale actomyosin rings appear to be the most promising cellular elements to accomplish this task. Here, we have adopted advanced encapsulation methods to study bundled actin filaments in GUVs and compare our results with theoretical modeling. By changing few key parameters, actin polymerization can be differentiated to resemble various types of networks in living cells. Importantly, we find membrane binding to be crucial for the robust condensation into a single actin ring in spherical vesicles, as predicted by theoretical considerations. Upon force generation by ATP-driven myosin motors, these ring-like actin structures contract and locally constrict the vesicle, forming furrow-like deformations. On the other hand, cortex-like actin networks are shown to induce and stabilize deformations from spherical shapes.

Cytoskeletal networks support and direct cell shape and guide intercellular transport, but relatively little is understood about the self-organization of cytoskeletal components on the scale of an entire cell. Here, authors use an in vitro system and observe the assembly of different types of actin networks and the condensation of membrane-bound actin into single rings.

Confinement Geometry Tunes Fascin-Actin Bundle Structures and Consequently the Shape of a Lipid Bilayer Vesicle

Depending on the physical and biochemical properties of actin-binding proteins, actin networks form different types of membrane protrusions at the cell periphery. Actin crosslinkers, which facilitate the interaction of actin filaments with one another, are pivotal in determining the mechanical properties and protrusive behavior of actin networks. Short crosslinkers such as fascin bundle F-actin to form rigid spiky filopodial protrusions. By encapsulation of fascin and actin in giant unilamellar vesicles (GUVs), we show that fascin-actin bundles cause various GUV shape changes by forming bundle networks or straight single bundles depending on GUV size and fascin concentration. We also show that the presence of a long crosslinker, α-actinin, impacts fascin-induced GUV shape changes and significantly impairs the formation of filopodia-like protrusions. Actin bundle-induced GUV shape changes are confirmed by light-induced disassembly of actin bundles leading to the reversal of GUV shape. Our study contributes to advancing the design of shape-changing minimal cells for better characterization of the interaction between lipid bilayer membranes and actin cytoskeleton.

Intersection To Overpass: Instance Segmentation On Filamentous Structures With An Orientation-Aware Neural Network And Terminus Pairing Algorithm

Filamentous structures play an important role in biological systems. Extracting individual filaments is fundamental for analyzing and quantifying related biological processes. However, segmenting filamentous structures at an instance level is hampered by their complex architecture, uniform appearance, and image quality. In this paper, we introduce an orientation-aware neural network, which contains six orientation-associated branches. Each branch detects filaments with specific range of orientations, thus separating them at junctions, and turning intersections to overpasses. A terminus pairing algorithm is also proposed to regroup filaments from different branches, and achieve individual filaments extraction. We create a synthetic dataset to train our network, and annotate real full resolution microscopy images of microtubules to test our approach. Our experiments have shown that our proposed method outperforms most existing approaches for filaments extraction. We also show that our approach works on other similar structures with a road network dataset.

Automated identification of flagella from videomicroscopy via the medial axis transform

Ubiquitous in eukaryotic organisms, the flagellum is a well-studied organelle that is well-known to be responsible for motility in a variety of organisms. Commonly necessitated in their study is the capability to image and subsequently track the movement of one or more flagella using videomicroscopy, requiring digital isolation and location of the flagellum within a sequence of frames. Such a process in general currently requires some researcher input, providing some manual estimate or reliance on an experiment-specific heuristic to correctly identify and track the motion of a flagellum. Here we present a fully-automated method of flagellum identification from videomicroscopy based on the fact that the flagella are of approximately constant width when viewed by microscopy. We demonstrate the effectiveness of the algorithm by application to captured videomicroscopy of Leishmania mexicana, a parasitic monoflagellate of the family Trypanosomatidae. ImageJ Macros for flagellar identification are provided, and high accuracy and remarkable throughput are achieved via this unsupervised method, obtaining results comparable in quality to previous studies of closely-related species but achieved without the need for precursory measurements or the development of a specialised heuristic, enabling in general the automated generation of digitised kinematic descriptions of flagellar beating from videomicroscopy.

Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX

Studies of how individual semi-flexible biopolymers and their network assemblies change over time reveal dynamical and mechanical properties important to the understanding of their function in tissues and living cells. Automatic tracking of biopolymer networks from fluorescence microscopy time-lapse sequences facilitates such quantitative studies. We present an open source software tool that combines a global and local correspondence algorithm to track biopolymer networks in 2D and 3D, using stretching open active contours. We demonstrate its application in fully automated tracking of elongating and intersecting actin filaments, detection of loop formation and constriction of tilted contractile rings in live cells, and tracking of network deformation under shear deformation.

Computational 3D imaging to quantify structural components and assembly of protein networks

Traditionally, protein structures have been described by the secondary structure architecture and fold arrangement. However, the relatively novel method of 3D confocal microscopy of fluorescent-protein-tagged networks in living cells allows resolving the detailed spatial organization of these networks. This provides new possibilities to predict network functionality, as structure and function seem to be linked at various scales. Here, we propose a quantitative approach using 3D confocal microscopy image data to describe protein networks based on their nano-structural characteristics. This analysis is constructed in four steps: (i) Segmentation of the microscopic raw data into a volume model and extraction of a spatial graph representing the protein network. (ii) Quantifying protein network gross morphology using the volume model. (iii) Quantifying protein network components using the spatial graph. (iv) Linking these two scales to obtain insights into network assembly. Here, we quantitatively describe the filamentous temperature sensitive Z protein network of the moss Physcomitrella patens and elucidate relations between network size and assembly details. Future applications will link network structure and functionality by tracking dynamic structural changes over time and comparing different states or types of networks, possibly allowing more precise identification of (mal) functions or the design of protein-engineered biomaterials for applications in regenerative medicine.

STATEMENT OF SIGNIFICANCE

Protein networks are highly complex and dynamic structures that play various roles in biological environments. Analyzing the detailed spatial structure of these networks may lead to new insight into biological functions and malfunctions. Here, we propose a tool set that extracts structural information at two scales of the protein network and allows therefore to address questions such as "how is the network built?" or "how networks grow?".

Revealing and Quantifying the Three-Dimensional Nano- and Microscale Structures in Self-Assembled Cellulose Microfibrils in Dispersions

Cellulose microfibrils (CMFs) are an important nanoscale building block in many novel biobased functional materials. The spatial nano- and microscale organization of the CMFs is a crucial factor for defining the properties of these materials. Here, we report for the first time a direct three-dimensional (3D) real-space analysis of individual CMFs and their networks formed after ultrahigh-shear-induced transient deagglomeration and self-assembly in a solvent. Using point-scanning confocal microscopy combined with tracking the centerlines of the fibrils and their junctions by a stretching open active contours method, we reveal that dispersions of the native CMFs assemble into highly heterogeneous networks of individual fibrils and bundles. The average network mesh size decreases with increasing CMF volume fraction. The cross-sectional width and the average length between the twists in the ribbon-shaped CMFs are directly determined and compared well with that of fibrils in the dried state. Finally, the generality of the fluorescent labeling and imaging approach on other CMF sources is illustrated. The unique ability to quantify in situ the multiscale structure in CMF dispersions provides a powerful tool for the correlation of process-structure-property relationship in cellulose-containing composites and dispersions.

A robust construction algorithm of the centerline skeleton for complex aortic vascular structure using computational fluid dynamics

Centerlines of blood vessels are useful tools to make important anatomical measurements (length, diameter, area), which cannot be accurately obtained using 2D images. In this paper a brand new method for centerline extraction of vascular trees is presented. By using computational fluid dynamics (CFD) we are able to obtain a robust and purely functional centerline allowing us to support better measurements than classic purely geometrical-based centerlines. We show that the CFD-based centerline is within a few pixels from the geometrical centerline where the latter is defined (far away from inlet/outlets and from the branches). We show that the centerline computed with our method is not affected by traditional errors of other classical volume-based algorithms such as topological thinning, and could be a potential alternative to be considered for future studies.

An integrated enhancement and reconstruction strategy for the quantitative extraction of actin stress fibers from fluorescence micrographs

BACKGROUND

The stress fibers are prominent organization of actin filaments that perform important functions in cellular processes such as migration, polarization, and traction force generation, and whose collective organization reflects the physiological and mechanical activities of the cells. Easily visualized by fluorescence microscopy, the stress fibers are widely used as qualitative descriptors of cell phenotypes. However, due to the complexity of the stress fibers and the presence of other actin-containing cellular features, images of stress fibers are relatively challenging to quantitatively analyze using previously developed approaches, requiring significant user intervention. This poses a challenge for the automation of their detection, segmentation, and quantitative analysis.

RESULT

Here we describe an open-source software package, SFEX (Stress Fiber Extractor), which is geared for efficient enhancement, segmentation, and analysis of actin stress fibers in adherent tissue culture cells. Our method made use of a carefully chosen image filtering technique to enhance filamentous structures, effectively facilitating the detection and segmentation of stress fibers by binary thresholding. We subdivided the skeletons of stress fiber traces into piecewise-linear fragments, and used a set of geometric criteria to reconstruct the stress fiber networks by pairing appropriate fiber fragments. Our strategy enables the trajectory of a majority of stress fibers within the cells to be comprehensively extracted. We also present a method for quantifying the dimensions of the stress fibers using an image gradient-based approach. We determine the optimal parameter space using sensitivity analysis, and demonstrate the utility of our approach by analyzing actin stress fibers in cells cultured on various micropattern substrates.

CONCLUSION

We present an open-source graphically-interfaced computational tool for the extraction and quantification of stress fibers in adherent cells with minimal user input. This facilitates the automated extraction of actin stress fibers from fluorescence images. We highlight their potential uses by analyzing images of cells with shapes constrained by fibronectin micropatterns. The method we reported here could serve as the first step in the detection and characterization of the spatial properties of actin stress fibers to enable further detailed morphological analysis.

Extracting microtubule networks from superresolution single-molecule localization microscopy data

Microtubule filaments form ubiquitous networks. However, quantitative analysis of this structure is difficult due to its complex architecture. A tool is given for the automated retrieval of microtubule filaments from superresolution microscopy images and used for a quantitative analysis of microtubule network architecture phenotypes in fibroblasts.

Microtubule filaments form ubiquitous networks that specify spatial organization in cells. However, quantitative analysis of microtubule networks is hampered by their complex architecture, limiting insights into the interplay between their organization and cellular functions. Although superresolution microscopy has greatly facilitated high-resolution imaging of microtubule filaments, extraction of complete filament networks from such data sets is challenging. Here we describe a computational tool for automated retrieval of microtubule filaments from single-molecule-localization–based superresolution microscopy images. We present a user-friendly, graphically interfaced implementation and a quantitative analysis of microtubule network architecture phenotypes in fibroblasts.

3D reconstruction of elastin fibres in coronary adventitia

A 3D reconstruction of individual fibres in vascular tissue is necessary to understand the microstructure properties of the vessel wall.  The objective of this study is to determine the 3D microstructure of elastin fibres in the adventitia of coronary arteries.  Quantification of fibre geometry is challenging due to the complex interwoven structure of the fibres.  In particular, accurate linking of gaps remains a significant challenge, and complex features such as long gaps and interwoven fibres have not been adequately addressed by current fibre reconstruction algorithms.  We use a novel line Laplacian deformation method, which better deals with fibre shape uncertainty to reconstruct elastin fibres in the coronary adventitia of five swine. A cost function, based on entropy and Euler Spiral, was used in the shortest path search. We find that mean diameter of elastin fibres is 1.67 ± 1.42 μm and fibre orientation is clustered around two major angles of 8.9˚ and 81.8˚.  Comparing with CT-FIRE, we find that our method gives more accurate estimation of fibre width.  To our knowledge, the measurements obtained using our algorithm represent the first investigation focused on the reconstruction of full elastin fibre length.  Our data provide a foundation for a 3D microstructural model of the coronary adventitia to elucidate the structure-function relationship of elastin fibres.

A Robust Actin Filaments Image Analysis Framework

The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a ‘cartoon’ part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the ‘cartoon’ image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts grown in two different conditions: static (control) and fluid shear stress. The proposed methodology exhibited higher sensitivity values and similar accuracy compared to state-of-the-art methods.

We propose a novel actin filaments cytoskeleton analysis framework that allows extracting quasi-straight individual fibers in a robust manner, and provides their respective position, orientation, and length as output. The proposed framework is defined as a three-steps processing sequence, that can explicitly cope with high-throughput imaging related issues, such as noise/artifacts presence and heavy blurring, and can similarly process artifacts-free and well-focused images.

Automatic optimal filament segmentation with sub-pixel accuracy using generalized linear models and B-spline level-sets

Biological filaments, such as actin filaments, microtubules, and cilia, are often imaged using different light-microscopy techniques. Reconstructing the filament curve from the acquired images constitutes the filament segmentation problem. Since filaments have lower dimensionality than the image itself, there is an inherent trade-off between tracing the filament with sub-pixel accuracy and avoiding noise artifacts. Here, we present a globally optimal filament segmentation method based on B-spline vector level-sets and a generalized linear model for the pixel intensity statistics. We show that the resulting optimization problem is convex and can hence be solved with global optimality. We introduce a simple and efficient algorithm to compute such optimal filament segmentations, and provide an open-source implementation as an ImageJ/Fiji plugin. We further derive an information-theoretic lower bound on the filament segmentation error, quantifying how well an algorithm could possibly do given the information in the image. We show that our algorithm asymptotically reaches this bound in the spline coefficients. We validate our method in comprehensive benchmarks, compare with other methods, and show applications from fluorescence, phase-contrast, and dark-field microscopy.

DeFiNe: an optimisation-based method for robust disentangling of filamentous networks

Thread-like structures are pervasive across scales, from polymeric proteins to root systems to galaxy filaments, and their characteristics can be readily investigated in the network formalism. Yet, network links usually represent only parts of filaments, which, when neglected, may lead to erroneous conclusions from network-based analyses. The existing alternatives to detect filaments in network representations require tuning of parameters over a large range of values and treat all filaments equally, thus, precluding automated analysis of diverse filamentous systems. Here, we propose a fully automated and robust optimisation-based approach to detect filaments of consistent intensities and angles in a given network. We test and demonstrate the accuracy of our solution with contrived, biological, and cosmic filamentous structures. In particular, we show that the proposed approach provides powerful automated means to study properties of individual actin filaments in their network context. Our solution is made publicly available as an open-source tool, "DeFiNe", facilitating decomposition of any given network into individual filaments.

Topology adaptive vessel network skeleton extraction with novel medialness measuring function

Vessel tree skeleton extraction is widely applied in vascular structure segmentation, however, conventional approaches often suffer from the adjacent interferences and poor topological adaptability. To avoid these problems, a robust, topology adaptive tree-like structure skeleton extraction framework is proposed in this paper. Specifically, to avoid the adjacent interferences, a local message passing procedure called Gaussian affinity voting (GAV) is proposed to realize adaptive scale-growing of vessel voxels. Then the medialness measuring function (MMF) based on GAV, namely GAV-MMF, is constructed to extract medialness patterns robustly. In order to improve topological adaptability, a level-set graph embedded with GAV-MMF is employed to build initial curve skeletons without any user interaction. Furthermore, the GAV-MMF is embedded in stretching open active contours (SOAC) to drive the initial curves to the expected location, maintaining smoothness and continuity. In addition, to provide an accurate and smooth final skeleton tree topology, topological checks and skeleton network reconfiguration is proposed. The continuity and scalability of this method is validated experimentally on synthetic and clinical images for multi-scale vessels. Experimental results show that the proposed method achieves acceptable topological adaptability for skeleton extraction of vessel trees.

SOAX: a software for quantification of 3D biopolymer networks

Filamentous biopolymer networks in cells and tissues are routinely imaged by confocal microscopy. Image analysis methods enable quantitative study of the properties of these curvilinear networks. However, software tools to quantify the geometry and topology of these often dense 3D networks and to localize network junctions are scarce. To fill this gap, we developed a new software tool called “SOAX”, which can accurately extract the centerlines of 3D biopolymer networks and identify network junctions using Stretching Open Active Contours (SOACs). It provides an open-source, user-friendly platform for network centerline extraction, 2D/3D visualization, manual editing and quantitative analysis. We propose a method to quantify the performance of SOAX, which helps determine the optimal extraction parameter values. We quantify several different types of biopolymer networks to demonstrate SOAX's potential to help answer key questions in cell biology and biophysics from a quantitative viewpoint.

Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density

Understanding single and collective cell motility in model environments is foundational to many current research efforts in biology and bioengineering. To elucidate subtle differences in cell behaviour despite cell-to-cell variability, we introduce an algorithm for tracking large numbers of cells for long time periods and present a set of physics-based metrics that quantify differences in cell trajectories. Our algorithm, termed automated contour-based tracking for in vitro environments (ACTIVE), was designed for adherent cell populations subject to nuclear staining or transfection. ACTIVE is distinct from existing tracking software because it accommodates both variability in image intensity and multi-cell interactions, such as divisions and occlusions. When applied to low-contrast images from live-cell experiments, ACTIVE reduced error in analysing cell occlusion events by as much as 43% compared with a benchmark-tracking program while simultaneously tracking cell divisions and resulting daughter-daughter cell relationships. The large dataset generated by ACTIVE allowed us to develop metrics that capture subtle differences between cell trajectories on different substrates. We present cell motility data for thousands of cells studied at varying densities on shape-memory-polymer-based nanotopographies and identify several quantitative differences, including an unanticipated difference between two 'control' substrates. We expect that ACTIVE will be immediately useful to researchers who require accurate, long-time-scale motility data for many cells.

Dynamic network morphology and tension buildup in a 3D model of cytokinetic ring assembly

During fission yeast cytokinesis, actin filaments nucleated by cortical formin Cdc12 are captured by myosin motors bound to a band of cortical nodes and bundled by cross-linking proteins. The myosin motors exert forces on the actin filaments, resulting in a net pulling of the nodes into a contractile ring, while cross-linking interactions help align actin filaments and nodes into a single bundle. We used these mechanisms in a three-dimensional computational model of contractile ring assembly, with semiflexible actin filaments growing from formins at cortical nodes, capturing of filaments by neighboring nodes, and cross-linking among filaments through attractive interactions. The model was used to predict profiles of actin filament density at the cell cortex, morphologies of condensing node-filament networks, and regimes of cortical tension by varying the node pulling force and strength of cross-linking among actin filaments. Results show that cross-linking interactions can lead to confinement of actin filaments at the simulated cortical boundary. We show that the ring-formation region in parameter space lies close to regions leading to clumps, meshworks or double rings, and stars/cables. Since boundaries between regions are not sharp, transient structures that resemble clumps, stars, and meshworks can appear in the process of ring assembly. These results are consistent with prior experiments with mutations in actin-filament turnover regulators, myosin motor activity, and changes in the concentration of cross-linkers that alter the morphology of the condensing network. Transient star shapes appear in some simulations, and these morphologies offer an explanation for star structures observed in prior experimental images. Finally, we quantify tension along actin filaments and forces on nodes during ring assembly and show that the mechanisms describing ring assembly can also drive ring constriction once the ring is formed.

Label-free evaluation of angiogenic sprouting in microengineered devices using ultrahigh-resolution optical coherence microscopy

Understanding the mechanism of angiogenesis could help to decipher wound healing and embryonic development and to develop better treatment for diseases such as cancer. Microengineered devices were developed to reveal the mechanisms of angiogenesis, but monitoring the angiogenic process nondestructively in these devices is a challenge. In this study, we utilized a label-free imaging technique, ultrahigh-resolution optical coherence microscopy (OCM), to evaluate angiogenic sprouting in a microengineered device. The OCM system was capable of providing ∼1.5-μm axial resolution and ∼2.3-μm transverse resolution. Three-dimensional (3-D) distribution of the sprouting vessels in the microengineered device was imaged over 0.6×0.6×0.5  mm3, and details such as vessel lumens and branching points were clearly visualized. An algorithm based on stretching open active contours was developed for tracking and segmenting the sprouting vessels in 3-D-OCM images. The lengths for the first-, second-, and third-order vessels were measured as 127.8±48.8  μm (n=8), 67.3±25.9  μm (n=9), and 62.5±34.7  μm (n=10), respectively. The outer diameters for the first-, second-, and third-order vessels were 13.2±1.0, 8.0±2.1, and 4.4±0.8  μm, respectively. These results demonstrate OCM as a promising tool for nondestructive and label-free evaluation of angiogenic sprouting in microengineered devices.