Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX

KnotResolver: tracking self-intersecting filaments in microscopy using directed graphs

MOTIVATION

Quantification of microscopy time series of in vitro reconstituted motor-driven microtubule transport in "gliding assays" is typically performed using computational object tracking tools. However, these are limited to non-intersecting and rod-like filaments.

RESULTS

Here, we describe a novel computational image-analysis pipeline, KnotResolver, to track image time series of highly curved self-intersecting looped filaments (knots) by resolving cross-overs. The code integrates filament segmentation and cross-over or "knot" identification based on directed graph representation, where nodes represent cross-overs and edges represent the path connecting them. The graphs are mapped back to contours and the distance to a reference minimized. The accuracy of contour detection is sub-pixel with a robustness to noise. We demonstrate the utility of KnotResolver by automatically quantifying "flagella-like" curvature dynamics and wave-like oscillations of clamped microtubules in a "gliding assay."

AVAILABILITY AND IMPLEMENTATION

The MATLAB-based source code is released as OpenSource and is available at https://github.com/CyCelsLab/MTKnotResolver.

PCP and Septins govern the polarized organization of the actin cytoskeleton during convergent extension

Convergent extension (CE) requires the coordinated action of the planar cell polarity (PCP) proteins1,2 and the actin cytoskeleton,3,4,5,6 but this relationship remains incompletely understood. For example, PCP signaling orients actomyosin contractions, yet actomyosin is also required for the polarized localization of PCP proteins.7,8 Moreover, the actin-regulating Septins play key roles in actin organization9 and are implicated in PCP and CE in frogs, mice, and fish5,6,10,11,12 but execute only a subset of PCP-dependent cell behaviors. Septin loss recapitulates the severe tissue-level CE defects seen after core PCP disruption yet leaves overt cell polarity intact.5 Together, these results highlight the general fact that cell movement requires coordinated action by distinct but integrated actin populations, such as lamella and lamellipodia in migrating cells13 or medial and junctional actin populations in cells engaged in apical constriction.14,15 In the context of Xenopus mesoderm CE, three such actin populations are important, a superficial meshwork known as the "node-and-cable" system,4,16,17,18 a contractile network at deep cell-cell junctions,6,19 and mediolaterally oriented actin-rich protrusions, which are present both superficially and deeply.4,19,20,21 Here, we exploited the amenability of the uniquely "two-dimensional" node and cable system to probe the relationship between PCP proteins, Septins, and the polarization of this actin network. We find that the PCP proteins Vangl2 and Prickle2 and Septins co-localize at nodes, and that the node and cable system displays a cryptic, PCP- and Septin-dependent anteroposterior (AP) polarity in its organization and dynamics.

Confined-microtubule assembly shapes three-dimensional cell wall structures in xylem vessels

Properly patterned deposition of cell wall polymers is prerequisite for the morphogenesis of plant cells. A cortical microtubule array guides the two-dimensional pattern of cell wall deposition. Yet, the mechanism underlying the three-dimensional patterning of cell wall deposition is poorly understood. In metaxylem vessels, cell wall arches are formed over numerous pit membranes, forming highly organized three-dimensional cell wall structures. Here, we show that the microtubule-associated proteins, MAP70-5 and MAP70-1, regulate arch development. The map70-1 map70-5 plants formed oblique arches in an abnormal orientation in pits. Microtubules fit the aperture of developing arches in wild-type cells, whereas microtubules in map70-1 map70-5 cells extended over the boundaries of pit arches. MAP70 caused the bending and bundling of microtubules. These results suggest that MAP70 confines microtubules within the pit apertures by altering the physical properties of microtubules, thereby directing the growth of pit arches in the proper orientation. This study provides clues to understanding how plants develop three-dimensional structure of cell walls.

The kinesin Kif21b regulates radial migration of cortical projection neurons through a non-canonical function on actin cytoskeleton

Completion of neuronal migration is critical for brain development. Kif21b is a plus-end-directed kinesin motor protein that promotes intracellular transport and controls microtubule dynamics in neurons. Here we report a physiological function of Kif21b during radial migration of projection neurons in the mouse developing cortex. In vivo analysis in mouse and live imaging on cultured slices demonstrate that Kif21b regulates the radial glia-guided locomotion of newborn neurons independently of its motility on microtubules. We show that Kif21b directly binds and regulates the actin cytoskeleton both in vitro and in vivo in migratory neurons. We establish that Kif21b-mediated regulation of actin cytoskeleton dynamics influences branching and nucleokinesis during neuronal locomotion. Altogether, our results reveal atypical roles of Kif21b on the actin cytoskeleton during migration of cortical projection neurons.

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.

Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks

Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology. However, they require water-tight surface meshes as inputs. Furthermore, adding vessels radii information on their graph compartments restricts their alignment along vascular centerlines. Here, we propose a novel graphing scheme that works with relaxed input requirements and intrinsically captures vessel radii information. The proposed approach is based on deforming geometric graphs constructed within vascular boundaries. Under a laplacian optimization framework, we assign affinity weights on the initial geometry that drives its iterative contraction toward vessels centerlines. We present a mechanism to decimate graph structure at each run and a convergence criterion to stop the process. A refinement technique is then introduced to obtain final vascular models. Our implementation is available on https://github.com/Damseh/VascularGraph. We benchmarked our results with that obtained using other efficient and state-of-the-art graphing schemes, validating on both synthetic and real angiograms acquired with different imaging modalities. The experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. Furthermore, it surpasses other techniques in providing representative models that capture all anatomical aspects of vascular structures.