CellMet: Extracting 3D shape and topology metrics from confluent cells within tissues.

A new method for reconstructing the 3D shape of single cells in fruit

In living systems, it is frequently stated that form follows function by virtue of evolutionary pressures on organism development, but in the study of how functions emerge at the cellular level, function often follows form. We study this chicken versus egg problem of emergent structure–property relationships in living systems in the context of primary human bone marrow stromal cells cultured in a variety of microenvironments that have been shown to cause distinct patterns of cell function and differentiation. Through analysis of a publicly available catalog of three-dimensional (3D) cell shape data, we introduce a family of metrics to characterize the ‘form’ of the cell populations that emerge from a variety of diverse microenvironments. In particular, measures of form are considered that are expected to have direct significance for cell function, signaling and metabolic activity: dimensionality, polarizability and capacitance. Dimensionality was assessed by an intrinsic measure of cell shape obtained from the polarizability tensor. This tensor defines ellipsoids for arbitrary cell shapes and the thinnest dimension of these ellipsoids, P1, defines a reference minimal scale for cells cultured in a 3D microenvironment. Polarizability governs the electric field generated by a cell, and determines the cell’s ability to detect electric fields. Capacitance controls the shape dependence of the rate at which diffusing molecules contact the surface of the cell, and this has great significance for inter-cellular signaling. These results invite new approaches for designing scaffolds which explicitly direct cell dimensionality, polarizability and capacitance to guide the emergence of new cell functions derived from the acquired form.

Deep learning has attracted increasing attention in brain imaging, but many neuroimaging data samples are small and fail to meet the training data requirements to optimize performance. In this study, we propose a deep transfer learning network based on Optimal Mass Transport (OMTNet) for 3D brain image classification using MRI scans from the UK Biobank. The major contributions of the OMTNet method include: a way to map 3D surface-based vertex-wise brain shape metrics, including cortical thickness, surface area, curvature, sulcal depth, and subcortical radial distance and surface Jacobian determinant metrics, onto 2D planar images for each MRI scan based on area-preserving mapping. Such that some popular 2D convolution neural networks pretrained on the ImageNet database, such as ResNet152 and DenseNet201, can be used for transfer learning of brain shape metrics. We used a score-fusion strategy to fuse all shape metrics and generate an ensemble classification. We tested the approach in a classification task conducted on 26k participants from the UK Biobank, using body mass index (BMI) thresholds as classification labels (normal vs. obese BMI). Ensemble classification accuracies of 72.8 ± 1.2% and 73.9 ± 2.3% were obtained for ResNet152 and DenseNet201 networks that used transfer learning, with 5.4–12.3% and 6.1–13.0% improvements relative to classifications based on single shape metrics, respectively. Transfer learning always outperformed direct learning and conventional linear support vector machines with 3.4–8.7% and 4.9–6.0% improvements in ensemble classification accuracies, respectively. Our proposed OMTNet method may offer a powerful transfer learning framework that can be extended to other vertex-wise brain structural/functional imaging measures.

A 3D cell shape found in sheets and tubes

Direct cell-cell communication with three-dimensional cell morphology on wrinkled microposts

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

3D Microscopy of Rod-Shaped Bacteria Reveals Roles of MreB in Diameter Control and Center-Line Curvature

Cell behaviors in multicellular organisms are coordinated via both diffusible molecules and by signals based on direct cell-cell contacts. The mode of cell communication used influences the signaling range. In many developing epithelia, contact-based Notch-Delta lateral inhibition signaling is used to pattern cell fates. While previous work revealed that cells can use protrusions to extend the range of Notch-Delta signaling to alter these patterns, this is not a general feature of epithelia. In addition, it is not known how the complex three-dimensional (3D) shapes of epithelial cells influence cell communication. In exploring this question, we show that epithelial cells at the Drosophila wing margin, which lack basal protrusions, contact different neighbors at different heights along their apico-basal axis, effectively increasing the number of neighbors each cell touches. To quantitatively assess this behavior, we develop a mathematical modeling framework (Multilayer Signaling Model) to simulate Notch-Delta signaling over data-derived 3D cell topologies. The model predicts that lateral cell surface signaling is essential to tune the spacing between sensory organ precursors (SOPs). In agreement, we show that perturbing cortical stiffness and cell tortuosity in vivo modifies SOP spacing. These results emphasize the importance of 3D cell geometry and topology in fine-tuning signaling range.

The spatial architecture of signaling pathways and the interaction with cell size and morphology are complex, but little understood. With the advances of single cell imaging and single cell biology, it becomes crucial to understand intracellular processes in time and space. Activation of cell surface receptors often triggers a signaling cascade including the activation of membrane-attached and cytosolic signaling components, which eventually transmit the signal to the cell nucleus. Signaling proteins can form steep gradients in the cytosol, which cause strong cell size dependence. We show that the kinetics at the membrane-cytosolic interface and the ratio of cell membrane area to the enclosed cytosolic volume change the behavior of signaling cascades significantly. We suggest an estimate of average concentration for arbitrary cell shapes depending on the cell volume and cell surface area. The normalized variance, known from image analysis, is suggested as an alternative measure to quantify the deviation from the average concentration. A mathematical analysis of signal transduction in time and space is presented, providing analytical solutions for different spatial arrangements of linear signaling cascades. Quantification of signaling time scales reveals that signal propagation is faster at the membrane than at the nucleus, while this time difference decreases with the number of signaling components in the cytosol. Our investigations are complemented by numerical simulations of non-linear cascades with feedback and asymmetric cell shapes. We conclude that intracellular signal propagation is highly dependent on cell geometry and, thereby, conveys information on cell size and shape to the nucleus.

Background. Following recent advances in bioimaging, high-resolution 3D models of biological structures are now generated rapidly and at low-cost. To utilise this data to address evolutionary and ecological questions, an array of tools has been developed to conduct 3D shape analysis and quantify topographic complexity. Here we focus particularly on shape techniques applied to irregular-shaped objects lacking clear homologous landmarks, and propose the new ‘alpha-shapes’ method for quantifying 3D shape complexity. Methods. We apply alpha-shapes to quantify shape complexity in the mammalian baculum as an example of a morphologically disparate structure. Micro- computed-tomography (μCT) scans of bacula were conducted. Bacula were binarised and converted into point clouds. Following application of a scaling factor to account for absolute differences in size, a suite of alpha-shapes was fitted to each specimen. An alpha shape is a formed from a subcomplex of the Delaunay triangulation of a given set of points, and ranges in refinement from a very coarse mesh (approximating convex hulls) to a very fine fit. ‘Optimal’ alpha was defined as the degree of refinement necessary in order for alpha-shape volume to equal CT voxel volume, and was taken as a metric of overall shape ‘complexity’. Results Our results show that alpha-shapes can be used to quantify interspecific variation in shape ‘complexity’ within biological structures of disparate geometry. The ‘stepped’ nature of alpha curves is informative with regards to the contribution of specific morphological features to overall shape ‘complexity’. Alpha-shapes agrees with other measures of topographic complexity (dissection index, Dirichlet normal energy) in identifying ursid bacula as having low shape complexity. However, alpha-shapes estimates mustelid bacula as possessing the highest topographic complexity, contrasting with other shape metrics. 3D fractal dimension is found to be an inappropriate metric of complexity when applied to bacula. Conclusions. The alpha-shapes methodology can be used to calculate ‘optimal’ alpha refinement as a proxy for shape ‘complexity’ without identifying landmarks. The implementation of alpha-shapes is straightforward, and is automated to process large datasets quickly. Beyond genital shape, we consider the alpha-shapes technique to hold considerable promise for new applications across evolutionary, ecological and palaeoecological disciplines.

BackgroundDuring plant and animal development, monolayer cell sheets display a stereotyped distribution of polygonal cell shapes. In interphase cells these shapes range from quadrilaterals to decagons, with a robust average of six sides per cell. In contrast, the subset of cells in mitosis exhibits a distinct distribution with an average of seven sides. It remains unclear whether this ‘mitotic shift’ reflects a causal relationship between increased polygonal sidedness and increased division likelihood, or alternatively, a passive effect of local proliferation on cell shape.MethodsWe use a combination of probabilistic analysis and mathematical modeling to predict the geometry of mitotic polygonal cells in a proliferating cell layer. To test these predictions experimentally, we use Flp-Out stochastic labeling in the Drosophila wing disc to induce single cell clones, and confocal imaging to quantify the polygonal topologies of these clones as a function of cellular age. For a more generic test in an idealized cell layer, we model epithelial sheet proliferation in a finite element framework, which yields a computationally robust, emergent prediction of the mitotic cell shape distribution.ResultsUsing both mathematical and experimental approaches, we show that the mitotic shift derives primarily from passive, non-autonomous effects of mitoses in neighboring cells on each cell’s geometry over the course of the cell cycle. Computationally, we predict that interphase cells should passively gain sides over time, such that cells at more advanced stages of the cell cycle will tend to have a larger number of neighbors than those at earlier stages. Validating this prediction, experimental analysis of randomly labeled epithelial cells in the Drosophila wing disc demonstrates that labeled cells exhibit an age-dependent increase in polygonal sidedness. Reinforcing these data, finite element simulations of epithelial sheet proliferation demonstrate in a generic framework that passive side-gaining is sufficient to generate a mitotic shift.ConclusionsTaken together, our results strongly suggest that the mitotic shift reflects a time-dependent accumulation of shared cellular interfaces over the course of the cell cycle. These results uncover fundamental constraints on the relationship between cell shape and cell division that should be general in adherent, polarized cell layers.

Nicotinic acid adenine dinucleotide phosphate (NAADP) has been implicated as an initial Ca2+ trigger in T cell Ca2+ signalling, but its role in formation of the immune synapse in CD4+ effector T cells has not been analysed. CD4+ T cells are activated by the interaction with peptide-MHCII complexes on the surface of antigen-presenting cells. Establishing a two-cell system including primary rat CD4+ T cells specific for myelin basic protein and rat astrocytes enabled us to mirror this activation process in vitro and to analyse Ca2+ signalling, cell shape changes and motility in T cells during formation and maintenance of the immune synapse. After immune synapse formation, T cells showed strong, antigen-dependent increases in free cytosolic calcium concentration ([Ca2+] i ). Analysis of cell shape and motility revealed rounding and immobilization of T cells depending on the amplitude of the Ca2+ signal. NAADP-antagonist BZ194 effectively blocked Ca2+ signals in T cells evoked by the interaction with antigen-presenting astrocytes. BZ194 reduced the percentage of T cells showing high Ca2+ signals thereby supporting the proposed trigger function of NAADP for global Ca2+ signalling. Taken together, the NAADP signalling pathway is further confirmed as a promising target for specific pharmacological intervention to modulate T cell activation.

Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution. Nevertheless, no GAN-based method has been proposed in the literature that can successfully represent, generate or translate 3D facial shapes (meshes). This can be primarily attributed to two facts, namely that (a) publicly available 3D face databases are scarce as well as limited in terms of sample size and variability (e.g., few subjects, little diversity in race and gender), and (b) mesh convolutions for deep networks present several challenges that are not entirely tackled in the literature, leading to operator approximations and model instability, often failing to preserve high-frequency components of the distribution. As a result, linear methods such as Principal Component Analysis (PCA) have been mainly utilized towards 3D shape analysis, despite being unable to capture non-linearities and high frequency details of the 3D face—such as eyelid and lip variations. In this work, we present 3DFaceGAN, the first GAN tailored towards modeling the distribution of 3D facial surfaces, while retaining the high frequency details of 3D face shapes. We conduct an extensive series of both qualitative and quantitative experiments, where the merits of 3DFaceGAN are clearly demonstrated against other, state-of-the-art methods in tasks such as 3D shape representation, generation, and translation.

The morphology of an individual particulate refers to its shape characteristics and size properties, which both play important roles for granular matter in physics, mechanics, chemistry, and biology. In this study, ellipsoidality is defined as a 3D shape index for evaluating particle roundness, and an explicit calculation method is applied. The dependences of 3D shape characteristics (aspect ratios, sphericity, and ellipsoidal degree) on particle size (ranges from 0.063 mm to 5.0 mm) are adequately investigated with the X-ray micro-computed microtomography (uCT) imaging for hundreds of thousands of particles of crushed and natural sands. This study focuses on comparing and evaluating the specific surface area and equivalent diameter, suggesting that particle segregation and changes in surface area may explain the strong dependence of particle shape on size. The correlation between different shape metrics was analyzed by comparing crushed sand with natural sand to provide theoretical support for material filling and mechanical behaviour. The significant differences in the microscale particle size indexes of different sands by single grading are used to provide data references for further analyses of the effect of material microscale on material properties in future discrete element particle simulations.

We present an aspect-graph approach to 3D object recognition where the definition of an aspect is motivated by its role in the subsequent recognition step. Specifically, we measure the similarity between two views by a 2D shape metric of similarity measuring the distance between the projected segmented shapes of the 3D object. This endows the viewing sphere with a metric which is used to group similar views into aspects, and to represent each aspect by a prototype. The same shape similarity metric is then used to rate the similarity between unknown views of unknown objects and stored prototypes to identify the object and its pose. The performance of this approach on a database of 18 objects each viewed in five degree increments along the ground viewing plane is demonstrated.