3D Cell Shape Research Articles

3D Micropatterned Traction Force Microscopy: A Technique to Control 3D Cell Shape While Measuring Cell-Substrate Force Transmission.

Cell shape and function are intimately linked, in a way that is mediated by the forces exerted between cells and their environment. The relationship between cell shape and forces has been extensively studied for cells seeded on flat 2D substrates, but not for cells in more physiological 3D settings. Here, a technique called 3D micropatterned traction force microscopy (3D-µTFM) to confine cells in 3D wells of defined shape, while simultaneously measuring the forces transmitted between cells and their microenvironment is demonstrated. This technique is based on the 3D micropatterning of polyacrylamide wells and on the calculation of 3D traction force from their deformation. With 3D-µTFM, it is shown that MCF10A breast epithelial cells exert defined, reproducible patterns of forces on their microenvironment, which can be both contractile and extensile. Cells switch from a global contractile to extensile behavior as their volume is reduced are further shown. The technique enables the quantitative study of cell mechanobiology with full access to 3D cellular forces while having accurate control over cell morphology and the mechanical conditions of the microenvironment.

SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization

The three-dimensional (3D) organization of cells determines tissue function and integrity, and changes markedly in development and disease. Cell-based simulations have long been used to define the underlying mechanical principles. However, high computational costs have so far limited simulations to either simplified cell geometries or small tissue patches. Here, we present SimuCell3D, an efficient open-source program to simulate large tissues in three dimensions with subcellular resolution, growth, proliferation, extracellular matrix, fluid cavities, nuclei and non-uniform mechanical properties, as found in polarized epithelia. Spheroids, vesicles, sheets, tubes and other tissue geometries can readily be imported from microscopy images and simulated to infer biomechanical parameters. Doing so, we show that 3D cell shapes in layered and pseudostratified epithelia are largely governed by a competition between surface tension and intercellular adhesion. SimuCell3D enables the large-scale in silico study of 3D tissue organization in development and disease at a great level of detail.

Spherical harmonics analysis reveals cell shape-fate relationships in zebrafish lateral line neuromasts.

Cell shape is a powerful readout of cell state, fate and function. We describe a custom workflow to perform semi-automated, 3D cell and nucleus segmentation, and spherical harmonics and principal components analysis to distill cell and nuclear shape variation into discrete biologically meaningful parameters. We apply these methods to analyze shape in the neuromast cells of the zebrafish lateral line system, finding that shapes vary with cell location and identity. The distinction between hair cells and support cells accounted for much of the variation, which allowed us to train classifiers to predict cell identity from shape features. Using transgenic markers for support cell subpopulations, we found that subtypes had different shapes from each other. To investigate how loss of a neuromast cell type altered cell shape distributions, we examined atoh1a mutants that lack hair cells. We found that mutant neuromasts lacked the cell shape phenotype associated with hair cells, but did not exhibit a mutant-specific cell shape. Our results demonstrate the utility of using 3D cell shape features to characterize, compare and classify cells in a living developing organism.

Disrupting bacterial cytoskeletal protein interaction partners modulates geometric localization and alters cell shape.

The shape a bacterial cell assumes is an essential component in its ability to interact with its physical environment. For many bacterial species, shape is genetically encoded in the action of a small number of cell shape determining proteins. These proteins guide the building of a rigid peptidoglycan cell wall of a given geometry. We use a computational imaging framework to reconstruct the 3D shape of individual cells from fluorescence microscopy images and determine where a secondary fluorescent probe is enriched. In the straight-rod bacterium Escherichia coli, the bacterial actin MreB is enriched in areas with low Gaussian curvature, avoiding the poles. Disrupting MreB's interactions with the transmembrane protein RodZ alters MreB's geometric localization and cells lose their characteristic rod-like morphology. In the helical-rod bacterium Helicobacter pylori, the bactofilin CcmA is preferentially enriched at regions of small positive Gaussian curvature along the outer or major helical axis. Disrupting CcmA interaction partners results in less CcmA on the membrane, altered geometric localization of CcmA, and loss of the characteristic helical morphology of cells. These results are consistent with a model of cell shape determination that includes proper geometric localization of bacterial cytoskeletal proteins.

Simulating 3D Cell Shape with the Cellular Potts Model.

Computer simulations have become a widely used method for the field of mechanobiology. An important question is whether one can predict the shape and forces of cells as a function of the extracellular environment. Different types of models have been described before to simulate cell and tissue shapes in structured environments. In this chapter, we give a brief overview of commonly used models and then describe the Cellular Potts Model, a lattice-based modelling framework, in more detail. We provide a hands-on guide on how to build a model that simulates the shape of a single cell on a micropattern in three dimensions in different open source software packages using the Cellular Potts framework. A simulation is set up with an initial configuration of generalized cells that change shape and position due to an energy function that incorporates cellular volume and surface area constraints as well as interaction energies between the generalized cells.

Large-scale analysis and computer modeling reveal hidden regularities behind variability of cell division patterns in Arabidopsis thaliana embryogenesis.

Noise plays a major role in cellular processes and in the development of tissues and organs. Several studies have examined the origin, the integration or the accommodation of noise in gene expression, cell growth and elaboration of organ shape. By contrast, much less is known about variability in cell division plane positioning, its origin and links with cell geometry, and its impact on tissue organization. Taking advantage of the first-stereotyped-then-variable division patterns in the embryo of the model plant Arabidopsis thaliana, we combined 3D imaging and quantitative cell shape and cell lineage analysis together with mathematical and computer modeling to perform a large-scale, systematic analysis of variability in division plane orientation. Our results reveal that, paradoxically, variability in cell division patterns of Arabidopsis embryos is accompanied by a progressive reduction of heterogeneity in cell shape topology. The paradox is solved by showing that variability operates within a reduced repertoire of possible division plane orientations that is related to cell geometry. We show that in several domains of the embryo, a recently proposed geometrical division rule recapitulates observed variable patterns, suggesting that variable patterns emerge from deterministic principles operating in a variable geometrical context. Our work highlights the importance of emerging patterns in the plant embryo under iterated division principles, but also reveal domains where deviations between rule predictions and experimental observations point to additional regulatory mechanisms.

Ptp61F integrates Hippo, TOR, and actomyosin pathways to control three-dimensional organ size.

Precise organ size control is fundamental for all metazoans, but how organ size is controlled in a three-dimensional (3D) way remains largely unexplored at the molecular level. Here, we screen and identify Drosophila Ptp61F as a pivotal regulator of organ size that integrates the Hippo pathway, TOR pathway, and actomyosin machinery. Pathologically, Ptp61F loss synergizes with RasV12 to induce tumorigenesis. Physiologically, Ptp61F depletion increases body size and drives neoplastic intestinal tumor formation and stem cell proliferation. Ptp61F also regulates cell contractility and myosin activation and controls 3D cell shape by reducing cell height and horizontal cell size. Mechanistically, Ptp61F forms a complex with Expanded (Ex) and increases endosomal localization of Ex and Yki. Furthermore, we demonstrate that PTPN2, the conserved human ortholog of Ptp61F, can functionally substitute for Ptp61F in Drosophila. Our work defines Ptp61F as an essential determinant that controls 3D organ size under both physiological and pathological conditions.

SHAPR predicts 3D cell shapes from 2D microscopic images

SummaryReconstruction of shapes and sizes of three-dimensional (3D) objects from two- dimensional (2D) information is an intensely studied subject in computer vision. We here consider the level of single cells and nuclei and present a neural network-based SHApe PRediction autoencoder. For proof-of-concept, SHAPR reconstructs 3D shapes of red blood cells from single view 2D confocal microscopy images more accurately than naïve stereological models and significantly increases the feature-based prediction of red blood cell types from F1 = 79% to F1 = 87.4%. Applied to 2D images containing spheroidal aggregates of densely grown human induced pluripotent stem cells, we find that SHAPR learns fundamental shape properties of cell nuclei and allows for prediction-based morphometry. Reducing imaging time and data storage, SHAPR will help to optimize and up-scale image-based high-throughput applications for biomedicine.

3D Shape of Epithelial Cells on Curved Substrates

It is widely recognized that the shape of epithelial cells is determined by the tension generated by the actomyosin cortex and the adhesion of cells to the substrate and to each other. To account for these biological and structural contributions to cell shape, different physical models have been proposed. However, an experimental procedure that would allow a validation of a minimal physical model for the shape of epithelial cells in 3D has not yet been proposed. In this study, we cultured MDCK epithelial cells on substrates with a sinusoidal profile, allowing us to measure the shape of the cells on various positive and negative curvatures. We found that MDCK cells are thicker in the valleys than on the crests of sinusoidal substrates. The influence of curvature on the shape of epithelial cells could not be understood with a model using only differential apical, basal and lateral surface energies. However, the addition of an apical line tension was sufficient to quantitatively account for the experimental measurements. The model also accounts for the shape of MDCK cells that overexpress E-cadherin. On the other hand, when reducing myosin II activity with blebbistatin, we measured a saturation of the difference in cell thickness between valleys and crests, suggesting the need for a term limiting large cell deformations. Our results show that a minimal model that accounts for epithelial cell shape needs to include an apical line tension in addition to differential surface energies, highlighting the importance of structures that produce anisotropic tension in epithelial cells, such as the actin belt linking adherens junctions. In the future, our experimental procedure could be used to test a wider range of physical models for the shape of epithelia in curved environments, including, for example, continuous models.

SpheresDT/Mpacts-PiCS: cell tracking and shape retrieval in membrane-labeled embryos.

MotivationUncovering the cellular and mechanical processes that drive embryo formation requires an accurate read out of cell geometries over time. However, automated extraction of 3D cell shapes from time-lapse microscopy remains challenging, especially when only membranes are labeled.ResultsWe present an image analysis framework for automated tracking and three-dimensional cell segmentation in confocal time lapses. A sphere clustering approach allows for local thresholding and application of logical rules to facilitate tracking and unseeded segmentation of variable cell shapes. Next, the segmentation is refined by a discrete element method simulation where cell shapes are constrained by a biomechanical cell shape model. We apply the framework on Caenorhabditis elegans embryos in various stages of early development and analyze the geometry of the 7- and 8-cell stage embryo, looking at volume, contact area and shape over time.Availability and implementationThe Python code for the algorithm and for measuring performance, along with all data needed to recreate the results is freely available at 10.5281/zenodo.5108416 and 10.5281/zenodo.4540092. The most recent version of the software is maintained at https://bitbucket.org/pgmsembryogenesis/sdt-pics.Supplementary information Supplementary data are available at Bioinformatics online.

Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of Drosophila melanogaster

Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues. Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a segmentation software that uses a particle-based active contour method. This program needs the fine-tuning of some hyperparameters that could present a long number of combinations, with the selection of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically selects the best possible parametrisation with which it can perform an accurate and non-supervised segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential of LimeSeg and optimise the parameters selection by adding automatisation. This methodology has been applied to three datasets of confocal images from Drosophila melanogaster, where a good convergence has been observed in the evaluation of the solutions. Our experimental results confirm the proper performing of the algorithm, whose segmented images have been compared to those manually obtained for the same tissues.

Integrated analysis of cell shape and movement in moving frame.

ABSTRACTThe cell's movement and morphological change are two interrelated cellular processes. An integrated analysis is needed to explore the relationship between them. However, it has been challenging to investigate them as a whole. The cell's trajectory can be described by its speed, curvature, and torsion. On the other hand, the three-dimensional (3D) cell shape can be studied by using a shape descriptor such as spherical harmonic (SH) descriptor, which is an extension of a Fourier transform in 3D space. We propose a novel method using parallel-transport (PT) to integrate these shape-movement data by using moving frames as the 3D-shape coordinate system. This moving frame is purely determined by the velocity vector. On this moving frame, the movement change will influence the coordinate system for shape analysis. By analyzing the change of the SH coefficients over time in the moving frame, we can observe the relationship between shape and movement. We illustrate the application of our approach using simulated and real datasets in this paper.

Improved 3D cellular morphometry of Caenorhabditis elegans embryos using a refractive index matching medium.

Cell shape change is one of the driving forces of animal morphogenesis, and the model organism Caenorhabditis elegans has played a significant role in analyzing the underlying mechanisms involved. The analysis of cell shape change requires quantification of cellular shape descriptors, a method known as cellular morphometry. However, standard C. elegans live imaging methods limit the capability of cellular morphometry in 3D, as spherical aberrations generated by samples and the surrounding medium misalign optical paths. Here, we report a 3D live imaging method for C. elegans embryos that minimized spherical aberrations caused by refractive index (RI) mismatch. We determined the composition of a refractive index matching medium (RIMM) for C. elegans live imaging. The 3D live imaging with the RIMM resulted in a higher signal intensity in the deeper cell layers. We also found that the obtained images improved the 3D cell segmentation quality. Furthermore, our 3D cellular morphometry and 2D cell shape simulation indicated that the germ cell precursor P4 had exceptionally high cortical tension. Our results demonstrate that the RIMM is a cost-effective solution to improve the 3D cellular morphometry of C. elegans. The application of this method should facilitate understanding of C. elegans morphogenesis from the perspective of cell shape changes.

Differences in the cell morphology and microfracture behaviour of tomato fruit (Solanum lycopersicum L.) tissues during ripening

Fresh tomato fruit (Solanum lycopersicum L.) is very susceptible to microdamage during post-harvest handling. This study reveals the differences in 3D cell morphology (e.g., shape, size, arrangement, and wall thickness) and microfailure behaviour of different tissues in the tomato fruit. At each ripening stage, the 3D shape, size and arrangement of cells in different tissues of a tomato fruit were investigated based on two microscopic orthogonal images. Additionally, the cell wall thickness was determined using transmission electron microscopy, and the microfailure mode of each tissue under shear and tension was quantitatively assessed based on the cell rupture rate in the crack growth path. The cells in different tissues of a tomato fruit showed obvious differences in the 3D shape and growth direction. Septa cells had the largest sizes. Cell wall thickness was closely associated with fruit ripeness and tissue type. Epidermal cell walls in the pink exocarp were the thickest compared with the other tissue cell walls. The cell rupture rate was dependent on the fruit ripening stage, the type of tissue and the direction/mode of the applied force (p < 0.05). Two failure modes, namely, cell rupture and separation, were observed in the different tissues of the tomato fruit at each ripening stage under shear and tension. This work provides a vital basis for modelling and simulating microscopic mechanical damage experienced by fresh tomato fruits due to excessive external forces during post-harvest handling.

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization.

The shape of a bacterium is important for its physiology. Many aspects of cell physiology such as cell motility, predation, and biofilm production can be affected by cell shape. Bacterial cells are three-dimensional (3D) objects, although they are rarely treated as such. Most microscopy techniques result in two-dimensional (2D) images leading to the loss of data pertaining to the actual 3D cell shape and localization of proteins. Certain shape parameters, such as Gaussian curvature (the product of the two principal curvatures), can only be measured in 3D because 2D images do not measure both principal curvatures. Additionally, not all cells lie flat when mounting and 2D imaging of curved cells may not accurately represent the shapes of these cells. Accurately measuring protein localization in 3D can help determine the spatial regulation and function of proteins. A forward convolution technique has been developed that uses the blurring function of the microscope to reconstruct 3D cell shapes and to accurately localize proteins. Here, a protocol for preparing and mounting samples for live cell imaging of bacteria in 3D both to reconstruct an accurate cell shape and to localize proteins is described. The method is based on simple sample preparation, fluorescent image acquisition, and MATLAB-based image processing. Many high-quality fluorescent microscopes can be simply modified to take these measurements. These cell reconstructions are computationally intensive and access to high-throughput computational resources is recommended, although not necessary. This method has been successfully applied to multiple bacterial species and mutants, fluorescent imaging modalities, and microscope manufacturers.

Regulating Mechanotransduction in Three Dimensions using Sub‐Cellular Scale, Crosslinkable Fibers of Controlled Diameter, Stiffness, and Alignment

AbstractThe extracellular matrix (ECM) is a complex 3D framework of macromolecules, which regulate cell bioactivity via chemical and physical properties. The ECM's physical properties, including stiffness and physical constraints to cell shape, regulate actomyosin cytoskeleton contractions, which induce signaling cascades influencing gene expression and cell fate. Engineering such bioactivity, a.k.a., mechanotransduction, has been mainly achieved by 2D platforms such as micropatterns. These platforms cause cytoskeletal contractions with apico‐basal polarity and can induce mechanotransduction that is unnatural to most cells in native ECMs. An effective method to engineer mechanotransduction in 3D is needed. This work creates FiberGel, a 3D artificial ECM comprised of sub‐cellular scale fibers. These microfibers can crosslink into defined microstructures with the fibers' diameter, stiffness, and alignment independently tuned. Most importantly, cells are blended amongst the fibers prior to crosslinking, leading to homogeneously cellularized scaffolds. Studies using mesenchymal stem cells showed that the microfibers' diameter, stiffness, and alignment regulate 3D cell shape and the nuclei translocation of transcriptional coactivators YAP/TAZ (yes‐associated protein/transcriptional coactivator), which enables the control of cell differentiation and tissue formation. A novel technology based on repeated stretching and folding is created to synthesize FiberGel. This 3D platform can significantly contribute to mechanotransduction research and applications.

Cell geometry determines symmetric and asymmetric division plane selection in Arabidopsis early embryos.

Plant tissue architecture and organ morphogenesis rely on the proper orientation of cell divisions. Previous attempts to predict division planes from cell geometry in plants mostly focused on 2D symmetric divisions. Using the stereotyped division patterns of Arabidopsis thaliana early embryogenesis, we investigated geometrical principles underlying plane selection in symmetric and in asymmetric divisions within complex 3D cell shapes. Introducing a 3D computational model of cell division, we show that area minimization constrained on passing through the cell centroid predicts observed divisions. Our results suggest that the positioning of division planes ensues from cell geometry and gives rise to spatially organized cell types with stereotyped shapes, thus underlining the role of self-organization in the developing architecture of the embryo. Our data further suggested the rule could be interpreted as surface minimization constrained by the nucleus position, which was validated using live imaging of cell divisions in the stomatal cell lineage.

Scutoids: Understanding the 3D Packing of Curved Epithelia

During development metazoa are shaped into complex 3D structures. This process is achieved using epithelial cells as building blocks. Thus, driven by internal/external stimuli, epithelial cells adopt a variety of shapes that affect their packing and the properties of the tissue and its morphology. In this context, the accepted view was that when tissues bend, cells change theirs shape from columnar (prism) to a “bottle shape” (frustum) hence assuming that the 3D packing properties remain unchanged along the apicobasal axis. In order to characterize and investigate the 3D packing and the shapes of cells in curved epithelia we mimicked computationally the architecture of tissues using computational geometry methods (Voronoi tessellation). To confirm the predictions of our model, we have investigated by microscopy means the 3D packing of the salivary gland of Drosophila (a model extensively used for tubulogenesis) as well as other tissues in different organisms. Finally, we have also developed a theoretical model based on energetic considerations to understand the biophysical origin of the 3D cellular organization. Our computational model predicts, and our experiments confirm, that, as the curvature of the tissue increases, prisms and frusta are not the only cell shapes that develop and reveals a previously undescribed geometrical shape: the scutoid. As a consequence, the apical and basal surfaces may have different packing topologies showing a neighbor interchange between cells along their apicobasal. Our energetics analysis reveals that scutoids allow tissues to minimize the packing energy and we propose that such geometrical shape is nature's solution to epithelial bending. 1) Scutoids are a geometrical solution to three-dimensional packing of epithelia, P. Gómez-G〈lvez et al., Nature Communications 9, 2960 (2018). 2) A 3D cell shape that enables tube formation, G. Blanchard, Nature 561, 182 (2018)

Inferring statistical properties of 3D cell geometry from 2D slices.

Although cell shape can reflect the mechanical and biochemical properties of the cell and its environment, quantification of 3D cell shapes within 3D tissues remains difficult, typically requiring digital reconstruction from a stack of 2D images. We investigate a simple alternative technique to extract information about the 3D shapes of cells in a tissue; this technique connects the ensemble of 3D shapes in the tissue with the distribution of 2D shapes observed in independent 2D slices. Using cell vertex model geometries, we find that the distribution of 2D shapes allows clear determination of the mean value of a 3D shape index. We analyze the errors that may arise in practice in the estimation of the mean 3D shape index from 2D imagery and find that typically only a few dozen cells in 2D imagery are required to reduce uncertainty below 2%. Even though we developed the method for isotropic animal tissues, we demonstrate it on an anisotropic plant tissue. This framework could also be naturally extended to estimate additional 3D geometric features and quantify their uncertainty in other materials.

Three-Dimensional Cell Geometry Controls Excitable Membrane Signaling in Dictyostelium Cells

Phosphatidylinositol (3–5)-trisphosphate (PtdInsP3) is known to propagate as waves on the plasma membrane and is related to the membrane-protrusive activities in Dictyostelium and mammalian cells. Although there have been a few attempts to study the three-dimensional (3D) dynamics of these processes, most studies have focused on the dynamics extracted from single focal planes. However, the relation between the dynamics and 3D cell shape remains elusive because of the lack of signaling information about the unobserved part of the membrane. Here, we show that PtdInsP3 wave dynamics are directly regulated by the 3D geometry (i.e., size and shape) of the plasma membrane. By introducing an analysis method that extracts the 3D spatiotemporal activities on the entire cell membrane, we show that PtdInsP3 waves self-regulate their dynamics within the confined membrane area. This leads to changes in speed, orientation, and pattern evolution, following the underlying excitability of the signal transduction system. Our findings emphasize the role of the plasma membrane topology in reaction-diffusion-driven biological systems and indicate its importance in other mammalian systems.