EXTRACTION AND ANALYSIS OF ACTIN NETWORKS BASED ON OPEN ACTIVE CONTOUR MODELS

Combining Image Restoration and Traction Force Microscopy to Study Extracellular Matrix-Dependent Keratin Filament Network Plasticity

Keratin intermediate filaments are dynamic cytoskeletal components that are responsible for tuning the mechanical properties of epithelial tissues. Although it is known that keratin filaments (KFs) are able to sense and respond to changes in the physicochemical properties of the local niche, a direct correlation of the dynamic three-dimensional network structure at the single filament level with the microenvironment has not been possible. Using conventional approaches, we find that keratin flow rates are dependent on extracellular matrix (ECM) composition but are unable to resolve KF network organization at the single filament level in relation to force patterns. We therefore developed a novel method that combines a machine learning-based image restoration technique and traction force microscopy to decipher the fine details of KF network properties in living cells grown on defined ECM patterns. Our approach utilizes Content-Aware Image Restoration (CARE) to enhance the temporal resolution of confocal fluorescence microscopy by at least five fold while preserving the spatial resolution required for accurate extraction of KF network structure at the single KF/KF bundle level. The restored images are used to segment the KF network, allowing numerical analyses of its local properties. We show that these tools can be used to study the impact of ECM composition and local mechanical perturbations on KF network properties and corresponding traction force patterns in size-controlled keratinocyte assemblies. We were thus able to detect increased curvature but not length of KFs on laminin-322 versus fibronectin. Photoablation of single cells in microprinted circular quadruplets revealed surprisingly little but still significant changes in KF segment length and curvature that were paralleled by an overall reduction in traction forces without affecting global network orientation in the modified cell groups irrespective of the ECM coating. Single cell analyses furthermore revealed differential responses to the photoablation that were less pronounced on laminin-332 than on fibronectin. The obtained results illustrate the feasibility of combining multiple techniques for multimodal monitoring and thereby provide, for the first time, a direct comparison between the changes in KF network organization at the single filament level and local force distribution in defined paradigms.

Quantitative mapping of keratin networks in 3D

Mechanobiology requires precise quantitative information on processes taking place in specific 3D microenvironments. Connecting the abundance of microscopical, molecular, biochemical, and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps needed for biophysical modeling is a particular challenge for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. To date, useful tools are available for the segmentation and modeling of actin filaments and microtubules but comprehensive tools for the mapping of intermediate filament organization are still lacking. In this work, we describe a workflow to model and examine the complete 3D arrangement of the keratin intermediate filament cytoskeleton in canine, murine, and human epithelial cells both, in vitro and in vivo. Numerical models are derived from confocal airyscan high-resolution 3D imaging of fluorescence-tagged keratin filaments. They are interrogated and annotated at different length scales using different modes of visualization including immersive virtual reality. In this way, information is provided on network organization at the subcellular level including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature, and orientation. We show that the comparison of these parameters helps to identify, in quantitative terms, similarities and differences of keratin network organization in epithelial cell types defining subcellular domains, notably basal, apical, lateral, and perinuclear systems. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.

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.

Automated segmentation and quantification of actin stress fibres undergoing experimentally induced changes

The actin cytoskeleton is a main component of cells and it is crucially involved in many physiological processes, e.g. cell motility. Changes in the actin organization can be effected by diseases or vice versa. Due to the nonuniform pattern, it is difficult to quantify reasonable features of the actin cytoskeleton for a significantly high cell number. Here, we present an approach capable to fully segment and analyse the actin cytoskeleton of 2D fluorescence microscopic images with a special focus on stress fibres. The extracted feature data include length, width, orientation and intensity distributions of all traced stress fibres. Our approach combines morphological image processing techniques and a trace algorithm in an iterative manner, classifying the segmentation result with respect to the width of the stress fibres and in nonfibre-like actin. This approach enables us to capture experimentally induced processes like the condensation or the collapse of the actin cytoskeleton. We successfully applied the algorithm to F-actin images of cells that were treated with the actin polymerization inhibitor latrunculin A. Furthermore, we verified the robustness of our algorithm by a sensitivity analysis of the parameters, and we benchmarked our algorithm against established methods. In summary, we present a new approach to segment actin stress fibres over time to monitor condensation or collapse processes.

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.

Extraction of Individual Filaments from 2D Confocal Microscopy Images of Flat Cells

A crucial step in understanding the architecture of cells and tissues from microscopy images, and consequently explain important biological events such as wound healing and cancer metastases, is the complete extraction and enumeration of individual filaments from the cellular cytoskeletal network. Current efforts at quantitative estimation of filament length distribution, architecture and orientation from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here we demonstrate the application of a new algorithm to reliably estimate centerlines of biological filament bundles and extract individual filaments from the centerlines by systematically disambiguating filament intersections. We utilize a filament enhancement step followed by reverse diffusion based filament localization and an integer programming based set combination to systematically extract accurate filaments automatically from microscopy images. Experiments on simulated and real confocal microscope images of flat cells (2D images) show efficacy of the new method.

Localizing and extracting filament distributions from microscopy images

Detailed quantitative measurements of biological filament networks represent a crucial step in understanding architecture and structure of cells and tissues, which in turn explain important biological events such as wound healing and cancer metastases. Microscopic images of biological specimens marked for different structural proteins constitute an important source for observing and measuring meaningful parameters of biological networks. Unfortunately, current efforts at quantitative estimation of architecture and orientation of biological filament networks from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here, we describe a new method for localizing and extracting filament distributions from 2D microscopy images of different modalities. The method combines a filter-based detection of pixels likely to contain a filament with a constrained reverse diffusion-based approach for localizing the filaments centrelines. We show with qualitative and quantitative experiments, using both simulated and real data, that the new method can provide more accurate centreline estimates of filament in comparison to other approaches currently available. In addition, we show the algorithm is more robust with respect to variations in the initial filter-based filament detection step often used. We demonstrate the application of the method in extracting quantitative parameters from confocal microscopy images of actin filaments and atomic force microscopy images of DNA fragments.

Quantitative analysis of F-actin redistribution in astrocytoma cells treated with candidate pharmaceuticals

Actin fibers (F-actin) control the shape and internal organization of cells, and generate force. It has been long appreciated that these functions are tightly coupled, and in some cases drive cell behavior and cell fate. The distribution and dynamics of F-actin is different in cancer versus normal cells and in response to small molecules, including actin-targeting natural products and anticancer drugs. Therefore, quantifying actin structural changes from high resolution fluorescence micrographs is necessary for further understanding actin cytoskeleton dynamics and phenotypic consequences of drug interactions on cells. We applied an artificial neural network algorithm, which used image intensity and anisotropy measurements, to quantitatively classify F-actin subcellular features into actin along the edges of cells, actin at the protrusions of cells, internal fibers and punctate signals. The algorithm measured significant increase in F-actin at cell edges with concomitant decrease in internal punctate actin in astrocytoma cells lacking functional neurofibromin and p53 when treated with three structurally-distinct anticancer small molecules: OSW1, Schweinfurthin A (SA) and a synthetic marine compound 23'-dehydroxycephalostatin 1. Distinctly different changes were measured in cells treated with the actin inhibitor cytochalasin B. These measurements support published reports that SA acts on F-actin in NF1(-/-) neurofibromin deficient cancer cells through changes in Rho signaling. Quantitative pattern analysis of cells has wide applications for understanding mechanisms of small molecules, because many anti-cancer drugs directly or indirectly target cytoskeletal proteins. Furthermore, quantitative information about the actin cytoskeleton may make it possible to further understand cell fate decisions using mathematically testable models.

3D actin network centerline extraction with multiple active contours

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels.

Efficient multiple object tracking using mutually repulsive active membranes

Studies of social and group behavior in interacting organisms require high-throughput analysis of the motion of a large number of individual subjects. Computer vision techniques offer solutions to specific tracking problems, and allow automated and efficient tracking with minimal human intervention. In this work, we adopt the open active contour model to track the trajectories of moving objects at high density. We add repulsive interactions between open contours to the original model, treat the trajectories as an extrusion in the temporal dimension, and show applications to two tracking problems. The walking behavior of Drosophila is studied at different population density and gender composition. We demonstrate that individual male flies have distinct walking signatures, and that the social interaction between flies in a mixed gender arena is gender specific. We also apply our model to studies of trajectories of gliding Myxococcus xanthus bacteria at high density. We examine the individual gliding behavioral statistics in terms of the gliding speed distribution. Using these two examples at very distinctive spatial scales, we illustrate the use of our algorithm on tracking both short rigid bodies (Drosophila) and long flexible objects (Myxococcus xanthus). Our repulsive active membrane model reaches error rates better than 5 x 10(-6) per fly per second for Drosophila tracking and comparable results for Myxococcus xanthus.

Localizing and extracting filament distributions from microscopy images

Detailed quantitative measurements of biological filament networks represent a crucial step in understanding architecture and structure of cells and tissues, which in turn explain important biological events such as wound healing and cancer metastases. Confocal microscope images of biological specimens marked for different structural proteins constitute an important source for observing and measuring meaningful parameters of biological networks. Unfortunately, current efforts at quantitative estimation of architecture and orientation of biological filament networks from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here we describe a new method for localizing and extracting filament distributions from 2D confocal microscopy images. The method combines a filter-based detection of pixels likely to contain a filament with a constrained reverse diffusion-based approach for localizing the filaments centrelines. We show with qualitative and quantitative experiments, using both simulated and real data, that the new method can provide more accurate centreline estimates of filament in comparison to other approaches currently available. In addition, we show the algorithm is more robust with respect to variations in the initial filter-based filament detection step often used. We demonstrate the application of the method in extracting quantitative parameters from an experiment that seeks to quantify the effects of carbon nanotubes on actin cytoskeleton in live HeLa cells. We show that their presence can disrupt the overall actin cytoskeletal organization in such cells.

A systems-biology approach to yeast actin cables

We focus on actin cables in yeast as a model system for understanding cytoskeletal organization and the workings of actin itself. In particular, we highlight quantitative approaches on the kinetics of actin-cable assembly and methods of measuring their morphology by image analysis. Actin cables described by these studies can span greater lengths than a thousand end-to-end actin-monomers. Because of this difference in length scales, control of the actin-cable system constitutes a junction between short-range interactions - among actin-monomers and nucleating, polymerization-facilitating, side-binding, severing, and cross-linking proteins - and the emergence of cell-scale physical form as embodied by the actin cables themselves.