Complex Cell Shapes Research Articles

The systems and interactions underpinning complex cell wall patterning.

Cell walls can confer amazing properties to plant cells, particularly if they have complex patterns. Complex cell wall patterns in the primary cell wall often lead to complex cell shapes, whereas in the secondary cell wall they lead to advanced material properties that prepare cells for mechanically demanding tasks. Not surprisingly, many of these structures are found in water transporting tissues. In this review, I compare the mechanisms controlling primary and secondary cell wall patterns, with emphasis on water transporting tissues and insights derived from modeling studies. Much of what we know about this is based on complex cell shapes and primary xylem patterns, leading to an emphasis on the Rho-of-plants - cortical microtubule - cellulose microfibril system for secondary cell wall patterning. There is a striking diversity of secondary cell wall patterns with important functional benefits, however, about which we know much less and that may develop in substantially different ways.

Generative modeling of living cells with SO(3)-equivariant implicit neural representations

Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data sets can be used to improve these methods. This requires the synthesis of cell shapes as well as corresponding microscopy images using generative models. To synthesize realistic living cell shapes, the shape representation used by the generative model should be able to accurately represent fine details and changes in topology, which are common in cells. These requirements are not met by 3D voxel masks, which are restricted in resolution, and polygon meshes, which do not easily model processes like cell growth and mitosis. In this work, we propose to represent living cell shapes as level sets of signed distance functions (SDFs) which are estimated by neural networks. We optimize a fully-connected neural network to provide an implicit representation of the SDF value at any point in a 3D+time domain, conditioned on a learned latent code that is disentangled from the rotation of the cell shape. We demonstrate the effectiveness of this approach on cells that exhibit rapid deformations (Platynereis dumerilii), cells that grow and divide (C. elegans), and cells that have growing and branching filopodial protrusions (A549 human lung carcinoma cells). A quantitative evaluation using shape features and Dice similarity coefficients of real and synthetic cell shapes shows that our model can generate topologically plausible complex cell shapes in 3D+time with high similarity to real living cell shapes. Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.

Mechanistic multiscale modelling of energy metabolism in human astrocytes reveals the impact of morphology changes in Alzheimer's Disease.

Astrocytes with their specialised morphology are essential for brain homeostasis as metabolic mediators between blood vessels and neurons. In neurodegenerative diseases such as Alzheimer's disease (AD), astrocytes adopt reactive profiles with molecular and morphological changes that could lead to the impairment of their metabolic support and impact disease progression. However, the underlying mechanisms of how the metabolic function of human astrocytes is impaired by their morphological changes in AD are still elusive. To address this challenge, we developed and applied a metabolic multiscale modelling approach integrating the dynamics of metabolic energy pathways and physiological astrocyte morphologies acquired in human AD and age-matched control brain samples. The results demonstrate that the complex cell shape and intracellular organisation of energetic pathways determine the metabolic profile and support capacity of astrocytes in health and AD conditions. Thus, our mechanistic approach indicates the importance of spatial orchestration in metabolism and allows for the identification of protective mechanisms against disease-associated metabolic impairments.

CAJAL enables analysis and integration of single-cell morphological data using metric geometry

High-resolution imaging has revolutionized the study of single cells in their spatial context. However, summarizing the great diversity of complex cell shapes found in tissues and inferring associations with other single-cell data remains a challenge. Here, we present CAJAL, a general computational framework for the analysis and integration of single-cell morphological data. By building upon metric geometry, CAJAL infers cell morphology latent spaces where distances between points indicate the amount of physical deformation required to change the morphology of one cell into that of another. We show that cell morphology spaces facilitate the integration of single-cell morphological data across technologies and the inference of relations with other data, such as single-cell transcriptomic data. We demonstrate the utility of CAJAL with several morphological datasets of neurons and glia and identify genes associated with neuronal plasticity in C. elegans. Our approach provides an effective strategy for integrating cell morphology data into single-cell omics analyses.

Interacting bactofilins impact cell shape of the MreB-less multicellular Rhodomicrobium vannielii.

Most non-spherical bacteria rely on the actin-like MreB cytoskeleton to control synthesis of a cell-shaping and primarily rod-like cell wall. Diverging from simple rod shape generally requires accessory cytoskeletal elements, which locally interfere with the MreB-guided cell wall synthesis. Conserved and widespread representatives of this accessory cytoskeleton are bactofilins that polymerize into static, non-polar bundles of filaments. Intriguingly, many species of the Actinobacteria and Rhizobiales manage to grow rod-like without MreB by tip extension, yet some of them still possess bactofilin genes, whose function in cell morphogenesis is unknown. An intricate representative of these tip-growing bacteria is Rhodomicrobium vannielii; a member of the hitherto genetically not tractable and poorly studied Hyphomicrobiaceae within the MreB-less Rhizobiales order. R. vannielii displays complex asymmetric cell shapes and differentiation patterns including filamentous hyphae to produce offspring and to build dendritic multicellular arrays. Here, we introduce techniques to genetically access R. vannielii, and we elucidate the role of bactofilins in its sophisticated morphogenesis. By targeted mutagenesis and fluorescence microscopy, protein interaction studies and peptidoglycan incorporation analysis we show that the R. vannielii bactofilins are associated with the hyphal growth zones and that one of them is essential to form proper hyphae. Another paralog is suggested to represent a novel hybrid and co-polymerizing bactofilin. Notably, we present R. vannielii as a powerful new model to understand prokaryotic cell development and control of multipolar cell growth in the absence of the conserved cytoskeletal element, MreB.

Porous structure design and properties of dental implants

At present, selective laser melting (SLM) 3D printing technology can accurately control the internal pore structure and complex cell shape. Three types of reticulated meshes with cubic, G7 and composite structure cell shapes were fabricated by the SLM 3D printing technology using Ti–6Al–4V alloy powders. The bone stresses around the implant and the stresses in the implant were analyzed by ANSYS finite element software, which comprehensively evaluated the effect of porous dental implants with different spatial porosity characteristics on osseointegration. The results show that porous dental implants with composite structure of pore characteristics have improved mechanical and biological properties and can better promote the growth and integration of bone tissue.

GliaMorph: a modular image analysis toolkit to quantify Müller glial cell morphology.

Cell morphology is crucial for all cell functions. This is particularly true for glial cells as they rely on complex shape to contact and support neurons. However, methods to quantify complex glial cell shape accurately and reproducibly are lacking. To address this, we developed the image analysis pipeline 'GliaMorph'. GliaMorph is a modular analysis toolkit developed to perform (1) image pre-processing, (2) semi-automatic region-of-interest selection, (3) apicobasal texture analysis, (4) glia segmentation, and (5) cell feature quantification. Müller glia (MG) have a stereotypic shape linked to their maturation and physiological status. Here, we characterized MG on three levels: (1) global image-level, (2) apicobasal texture, and (3) regional apicobasal vertical-to-horizontal alignment. Using GliaMorph, we quantified MG development on a global and single-cell level, showing increased feature elaboration and subcellular morphological rearrangement in the zebrafish retina. As proof of principle, we analysed expression changes in a mouse glaucoma model, identifying subcellular protein localization changes in MG. Together, these data demonstrate that GliaMorph enables an in-depth understanding of MG morphology in the developing and diseased retina.

Mechano-chemical regulation of complex cell shape formation: Epidermal pavement cells-A case study.

All plant cells are encased by walls, which provide structural support and control their morphology. How plant cells regulate the deposition of the wall to generate complex shapes is a topic of ongoing research. Scientists have identified several model systems, the epidermal pavement cells of cotyledons and leaves being an ideal platform to study the formation of complex cell shapes. These cells indeed grow alternating protrusions and indentations resulting in jigsaw puzzle cell shapes. How and why these cells adopt such shapes has shown to be a challenging problem to solve, notably because it involves the integration of molecular and mechanical regulation together with cytoskeletal dynamics and cell wall modifications. In this review, we highlight some recent progress focusing on how these processes may be integrated at the cellular level along with recent quantitative morphometric approaches.

CrvA and CrvB form a curvature-inducing module sufficient to induce cell-shape complexity in Gram-negative bacteria.

Bacterial species have diverse cell shapes that enable motility, colonization, and virulence. The cell wall defines bacterial shape and is primarily built by two cytoskeleton-guided synthesis machines, the elongasome and the divisome. However, the mechanisms producing complex shapes, like the curved-rod shape of Vibrio cholerae, are incompletely defined. Previous studies have reported that species-specific regulation of cytoskeleton-guided machines enables formation of complex bacterial shapes such as cell curvature and cellular appendages. In contrast, we report that CrvA and CrvB are sufficient to induce complex cell shape autonomously of the cytoskeleton in V. cholerae. The autonomy of the CrvAB module also enables it to induce curvature in the Gram-negative species Escherichia coli, Pseudomonas aeruginosa, Caulobacter crescentus, and Agrobacterium tumefaciens. Using inducible gene expression, quantitative microscopy, and biochemistry we show that CrvA and CrvB circumvent the need for patterning via cytoskeletal elements by regulating each other to form an asymmetrically-localized, periplasmic structure that directly binds to the cell wall. The assembly and disassembly of this periplasmic structure enables dynamic changes in cell shape. Bioinformatics indicate that CrvA and CrvB may have diverged from a single ancestral hybrid protein. Using fusion experiments in V. cholerae, we find that a synthetic CrvA/B hybrid protein is sufficient to induce curvature on its own, but that expression of two distinct proteins, CrvA and CrvB, promotes more rapid curvature induction. We conclude that morphological complexity can arise independently of cell shape specification by the core cytoskeleton-guided synthesis machines.

3D Bioprinting-Based Vascularized Tissue Models Mimicking Tissue-Specific Architecture and Pathophysiology for in vitro Studies.

A wide variety of experimental models including 2D cell cultures, model organisms, and 3D in vitro models have been developed to understand pathophysiological phenomena and assess the safety and efficacy of potential therapeutics. In this sense, 3D in vitro models are an intermediate between 2D cell cultures and animal models, as they adequately reproduce 3D microenvironments and human physiology while also being controllable and reproducible. Particularly, recent advances in 3D in vitro biomimicry models, which can produce complex cell structures, shapes, and arrangements, can more similarly reflect in vivo conditions than 2D cell culture. Based on this, 3D bioprinting technology, which enables to place the desired materials in the desired locations, has been introduced to fabricate tissue models with high structural similarity to the native tissues. Therefore, this review discusses the recent developments in this field and the key features of various types of 3D-bioprinted tissues, particularly those associated with blood vessels or highly vascularized organs, such as the heart, liver, and kidney. Moreover, this review also summarizes the current state of the three categories: (1) chemical substance treatment, (2) 3D bioprinting of lesions, and (3) recapitulation of tumor microenvironments (TME) of 3D bioprinting-based disease models according to their disease modeling approach. Finally, we propose the future directions of 3D bioprinting approaches for the creation of more advanced in vitro biomimetic 3D tissues, as well as the translation of 3D bioprinted tissue models to clinical applications.

Embryonic Tissues as Active Foams.

The physical state of embryonic tissues emerges from non-equilibrium, collective interactions among constituent cells. Cellular jamming, rigidity transitions and characteristics of glassy dynamics have all been observed in multicellular systems, but it is unclear how cells control these emergent tissue states and transitions, including tissue fluidization. Combining computational and experimental methods, here we show that tissue fluidization in posterior zebrafish tissues is controlled by the stochastic dynamics of tensions at cell-cell contacts. We develop a computational framework that connects cell behavior to embryonic tissue dynamics, accounting for the presence of extracellular spaces, complex cell shapes and cortical tension dynamics. We predict that tissues are maximally rigid at the structural transition between confluent and non-confluent states, with actively-generated tension fluctuations controlling stress relaxation and tissue fluidization. By directly measuring strain and stress relaxation, as well as the dynamics of cell rearrangements, in elongating posterior zebrafish tissues, we show that tension fluctuations drive active cell rearrangements that fluidize the tissue. These results highlight a key role of non-equilibrium tension dynamics in developmental processes.

Mathematical modelling in cell migration: tackling biochemistry in changing geometries.

Directed cell migration poses a rich set of theoretical challenges. Broadly, these are concerned with (1) how cells sense external signal gradients and adapt; (2) how actin polymerisation is localised to drive the leading cell edge and Myosin-II molecular motors retract the cell rear; and (3) how the combined action of cellular forces and cell adhesion results in cell shape changes and net migration. Reaction-diffusion models for biological pattern formation going back to Turing have long been used to explain generic principles of gradient sensing and cell polarisation in simple, static geometries like a circle. In this minireview, we focus on recent research which aims at coupling the biochemistry with cellular mechanics and modelling cell shape changes. In particular, we want to contrast two principal modelling approaches: (1) interface tracking where the cell membrane, interfacing cell interior and exterior, is explicitly represented by a set of moving points in 2D or 3D space and (2) interface capturing. In interface capturing, the membrane is implicitly modelled analogously to a level line in a hilly landscape whose topology changes according to forces acting on the membrane. With the increased availability of high-quality 3D microscopy data of complex cell shapes, such methods will become increasingly important in data-driven, image-based modelling to better understand the mechanochemistry underpinning cell motion.

Wavy walls built by nanofilaments

Plant Science In the model plant Arabidopsis , pavement cells fit together with the lobes and curves of jigsaw puzzle pieces. Such complex cell shapes, in plants, were generally thought to be driven by turgor pressure. Haas et al. now show that the extracellular cell wall can actively shape the cell it contains without relying on turgor pressure. Nanofilaments of pectin homogalacturonan in the cell wall shift between crystalline and anisotropic phases according to whether they are methylated. The shift in form drives changes in cell wall shape that stand independent of turgor pressure. Science , this issue p. [1003][1] [1]: /lookup/doi/10.1126/science.aaz5103

Pectin homogalacturonan nanofilament expansion drives morphogenesis in plant epidermal cells.

The process by which plant cells expand and gain shape has presented a challenge for researchers. Current models propose that these processes are driven by turgor pressure acting on the cell wall. Using nanoimaging, we show that the cell wall contains pectin nanofilaments that possess an intrinsic expansion capacity. Additionally, we use growth models containing such structures to show that a complex plant cell shape can derive from chemically induced local and polarized expansion of the pectin nanofilaments without turgor-driven growth. Thus, the plant cell wall, outside of the cell itself, is an active participant in shaping plant cells. Extracellular matrix function may similarly guide cell shape in other kingdoms, including Animalia.

Basic Proline-Rich Protein-Mediated Microtubules Are Essential for Lobe Growth and Flattened Cell Geometry.

Complex cell shapes are generated first by breaking symmetry, and subsequent polar growth. Localized bending of anticlinal walls initiates lobe formation in the epidermal pavement cells of cotyledons and leaves, but how the microtubule cytoskeleton mediates local cell growth, and how plant pavement cells benefit from adopting jigsaw puzzle-like shapes, are poorly understood. In Arabidopsis (Arabidopsis thaliana), the basic Pro-rich protein (BPP) microtubule-associated protein family comprises seven members. We analyzed lobe morphogenesis in cotyledon pavement cells of a BPP1;BPP2;BPP5 triple knockout mutant. New image analysis methods (MtCurv and BQuant) showed that anticlinal microtubule bundles were significantly reduced and cortical microtubules that fan out radially across the periclinal wall did not enrich at the convex side of developing lobes. Despite these microtubule defects, new lobes were initiated at the same frequency as in wild-type cells, but they did not expand into well-defined protrusions. Eventually, mutant cells formed nearly polygonal shapes and adopted concentric microtubule patterns. The mutant periclinal cell wall bulged outward. The radius of the calculated inscribed circle of the pavement cells, a proposed proxy for maximal stress in the cell wall, was consistently larger in the mutant cells during cotyledon development, and correlated with an increase in cell height. These bpp mutant phenotypes provide genetic and cell biological evidence that initiation and growth of lobes are distinct morphogenetic processes, and that interdigitated cell geometry effectively suppresses large outward bulging of pavement cells.

A specialized MreB-dependent cell wall biosynthetic complex mediates the formation of stalk-specific peptidoglycan in Caulobacter crescentus.

Many bacteria have complex cell shapes, but the mechanisms producing their distinctive morphologies are still poorly understood. Caulobacter crescentus, for instance, exhibits a stalk-like extension that carries an adhesive holdfast mediating surface attachment. This structure forms through zonal peptidoglycan biosynthesis at the old cell pole and elongates extensively under phosphate-limiting conditions. We analyzed the composition of cell body and stalk peptidoglycan and identified significant differences in the nature and proportion of peptide crosslinks, indicating that the stalk represents a distinct subcellular domain with specific mechanical properties. To identify factors that participate in stalk formation, we systematically inactivated and localized predicted components of the cell wall biosynthetic machinery of C. crescentus. Our results show that the biosynthesis of stalk peptidoglycan involves a dedicated peptidoglycan biosynthetic complex that combines specific components of the divisome and elongasome, suggesting that the repurposing of preexisting machinery provides a straightforward means to evolve new morphological traits.

Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA).

ABSTRACTQuantifying cell morphology is fundamental to the statistical study of cell populations, and can help unravel mechanisms underlying cell and tissue morphogenesis. Current methods, however, require extensive human intervention, are highly parameter sensitive, or produce metrics that are difficult to interpret biologically. We therefore developed a method, lobe contribution elliptical Fourier analysis (LOCO-EFA), which generates from digitalised two-dimensional cell outlines meaningful descriptors that can be directly matched to morphological features. This is shown by studying well-defined geometric shapes as well as actual biological cells from plant and animal tissues. LOCO-EFA provides a tool to phenotype efficiently and objectively populations of cells, here demonstrated by applying it to the complex shaped pavement cells of Arabidopsis thaliana wild-type and speechless leaves, and Drosophila amnioserosa cells. To validate our method's applicability to large populations, we analysed computer-generated tissues. By controlling in silico cell shape, we explored the potential impact of cell packing on individual cell shape, quantifying through LOCO-EFA deviations between the specified shape of single cells in isolation and the resultant shape when they interact within a confluent tissue.

Why plants make puzzle cells, and how their shape emerges.

The shape and function of plant cells are often highly interdependent. The puzzle-shaped cells that appear in the epidermis of many plants are a striking example of a complex cell shape, however their functional benefit has remained elusive. We propose that these intricate forms provide an effective strategy to reduce mechanical stress in the cell wall of the epidermis. When tissue-level growth is isotropic, we hypothesize that lobes emerge at the cellular level to prevent formation of large isodiametric cells that would bulge under the stress produced by turgor pressure. Data from various plant organs and species support the relationship between lobes and growth isotropy, which we test with mutants where growth direction is perturbed. Using simulation models we show that a mechanism actively regulating cellular stress plausibly reproduces the development of epidermal cell shape. Together, our results suggest that mechanical stress is a key driver of cell-shape morphogenesis.

Finite Element Modeling of Shape Changes in Plant Cells.

Mechanical modeling of plant cells using finite element methods serves to simulate the behavior of complex cell shapes with the aim to understand biological functioning

Cell shape characterization and classification with discrete Fourier transforms and self-organizing maps.

Cells in their natural environment often exhibit complex kinetic behavior and radical adjustments of their shapes. This enables them to accommodate to short- and long-term changes in their surroundings under physiological and pathological conditions. Intravital multi-photon microscopy is a powerful tool to record this complex behavior. Traditionally, cell behavior is characterized by tracking the cells' movements, which yields numerous parameters describing the spatiotemporal characteristics of cells. Cells can be classified according to their tracking behavior using all or a subset of these kinetic parameters. This categorization can be supported by the a priori knowledge of experts. While such an approach provides an excellent starting point for analyzing complex intravital imaging data, faster methods are required for automated and unbiased characterization. In addition to their kinetic behavior, the 3D shape of these cells also provide essential clues about the cells' status and functionality. New approaches that include the study of cell shapes as well may also allow the discovery of correlations amongst the track- and shape-describing parameters. In the current study, we examine the applicability of a set of Fourier components produced by Discrete Fourier Transform (DFT) as a tool for more efficient and less biased classification of complex cell shapes. By carrying out a number of 3D-to-2D projections of surface-rendered cells, the applied method reduces the more complex 3D shape characterization to a series of 2D DFTs. The resulting shape factors are used to train a Self-Organizing Map (SOM), which provides an unbiased estimate for the best clustering of the data, thereby characterizing groups of cells according to their shape. We propose and demonstrate that such shape characterization is a powerful addition to, or a replacement for kinetic analysis. This would make it especially useful in situations where live kinetic imaging is less practical or not possible at all. © 2017 International Society for Advancement of Cytometry.