Syndecan Regulates Cellular Morphogenesis in Cooperation with the Netrin Guidance Pathway and Rho-family GTPases

The establishment of complex cell shapes is essential for specific cellular functions, and thus critical in animal development and physiology. Heparan sulfate proteoglycans (HSPGs) are conserved glycoproteins that regulate interactions between extracellular signals and their receptors, to orchestrate morphogenetic events and elicit cellular responses. Although HSPG-regulated pathways have been implicated in regulating the guidance of neuronal migrations, whether HSPGs regulate earlier aspects of cellular development that dictate cell shape remains unknown. HSPGs consist of a protein core (e.g., Syndecan, Perlecan, Glypican, etc.) with attached heparan sulfate (HS) glycosaminoglycan chains, which are synthesized by glycosyltransferases of the exostosin family. Using mutations in the two C. elegans HS glycosyltransferases genes, rib-1 and rib-2, we reveal that HSPGs control the number of cellular projections in the epithelial excretory canal cell, which can form more than its normal four canals in these mutants. We identify SDN-1/Syndecan as the key HSPG that regulates the number of excretory canal cell projections in a cell-autonomous manner. We also find that Syndecan and guidance receptors for Netrin function in the same pathway to restrict the number of cellular projections. Furthermore, we show that the formation of extra projections in the absence of Syndecan requires the conserved Rho-family GTPases CED-10/Rac and MIG-2/RhoG. Our findings not only contribute to understanding the roles of conserved HSPGs in cellular morphogenetic events, but also reveal the existence of an HSPG-regulated system operating to guarantee that a precise number of cellular projections is established during cell development. Given the evolutionary conservation of developmental mechanisms and the molecules implicated, this work provides information relevant to understanding the cellular and molecular bases of the development of precise cellular morphologies in varied cell types across animals.

FRaeppli, a multispectral imaging toolbox for cell tracing and dense tissue analysis in zebrafish

Visualizing cell shapes, interactions and lineages of differentiating cells is instrumental for understanding organ development and repair. Across species, strategies for stochastic multicolour labelling have greatly facilitated tracking cells in in vivo and mapping neuronal connectivity. Nevertheless, integrating multi-fluorophore information into the context of developing tissues in zebrafish is challenging given their cytoplasmic localization and spectral incompatibility with commonly used fluorescent markers. Here, we developed FRaeppli (Fish-Raeppli) expressing bright membrane- or nuclear-targeted fluorescent proteins for efficient cell shape analysis and tracking. High spatiotemporal activation flexibility is provided by the Gal4/UAS system together with Cre/lox and/or PhiC31integrase. The distinct spectra of the FRaeppli fluorescent proteins allow simultaneous imaging with GFP and infrared subcellular reporters or tissue landmarks. By tailoring hyperspectral protocols for time-efficient acquisition, we demonstrate FRaeppli s suitability for live imaging of complex internal organs, like the liver. Combining FRaeppli with polarity markers revealed previously unknown canalicular topologies between differentiating hepatocytes, reminiscent of the mammalian liver, suggesting shared developmental mechanisms. The multispectral FRaeppli toolbox thus enables the comprehensive analysis of intricate cellular morphologies, topologies and tissue lineages at single-cell resolution in zebrafish.

A novel deep learning-based 3D cell segmentation framework for future image-based disease detection

Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell membrane images. Existing approaches also require fine-tuning multiple manually selected hyperparameters on the new datasets. We develop a deep learning-based 3D cell segmentation pipeline, 3DCellSeg, to address these challenges. Compared to the existing methods, our approach carries the following novelties: (1) a robust two-stage pipeline, requiring only one hyperparameter; (2) a light-weight deep convolutional neural network (3DCellSegNet) to efficiently output voxel-wise masks; (3) a custom loss function (3DCellSeg Loss) to tackle the clumped cell problem; and (4) an efficient touching area-based clustering algorithm (TASCAN) to separate 3D cells from the foreground masks. Cell segmentation experiments conducted on four different cell datasets show that 3DCellSeg outperforms the baseline models on the ATAS (plant), HMS (animal), and LRP (plant) datasets with an overall accuracy of 95.6%, 76.4%, and 74.7%, respectively, while achieving an accuracy comparable to the baselines on the Ovules (plant) dataset with an overall accuracy of 82.2%. Ablation studies show that the individual improvements in accuracy is attributable to 3DCellSegNet, 3DCellSeg Loss, and TASCAN, with the 3DCellSeg demonstrating robustness across different datasets and cell shapes. Our results suggest that 3DCellSeg can serve a powerful biomedical and clinical tool, such as histo-pathological image analysis, for cancer diagnosis and grading.

Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods

Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, hyperspectral microscope imaging (HMI) was employed for this study. The mesocarp area with 20× magnification of blueberries was selectively imaged with a Fabry–Perot interferometer HMI system of 400–1000 nm wavelengths, resulting in 281 hypercubes of parenchyma cells in a resolution of 968 × 608 × 300 pixels. After properly processing each hypercube of parenchyma cells in a blueberry, the cell image with different firmness was examined based on parenchyma cell shape, cell wall segment, cell-to-cell adhesion, and size of intercellular spaces. Spectral cell characteristics of firmness were also sought based on the spectral profile of cell walls with different image preprocessing methods. The study found that softer blueberries (1.96–3.92 N) had more irregular cell shapes, lost cell-to-cell adhesion, loosened and round cell wall segments, large intercellular spaces, and cell wall colors that were more red than the firm blueberries (6.86–8.83 N). Even though berry-to-berry (or image-to-image) variations of the characteristics turned out large, the deep learning model with spatial and spectral features of blueberry cells demonstrated the potential for blueberry firmness classification with Matthew’s correlation coefficient of 73.4% and accuracy of 85% for test set.

The Effect of Design Parameters on Mechanical Characteristics of Porous CoCrMo Scaffold Manufactured by Additive Manufacturing

Metallic orthopedic implants to replace or generate lost bones caused by traumatic road traffic injuries often failed prematurely after surgery. Bone resorption caused by stress shielding of metallic implants became a main concern as it can potentially lead to bone implant failure. Metallic scaffold designed in porous structures fabricated using additive manufacturing (AM) are widely used as bone implant, since the elastic modulus of the scaffolds can easily tailored according to the bone properties, and the large surfaces are beneficial to cell in-growth. The microarchitecture of scaffold can control their mechanical and biological properties, but it is found that there is lack of systematic approach to select a cell topology with full perspective requirements of bone implant. This paper presents a systematic approach of design space mapping for two CoCrMo unit cell shapes namely square and diamond to understand the relationship between geometrical parameters with additive manufacturing limitation, mechanical and bone ingrowth requirements. The compressive response of the components was simulated by finite element analysis and the influence of design parameters on the scaffold behaviour was compared theoretically with Gibson and Ashby model. The FEA give prediction for effective elastic modulus of 3 GPa to 4.8 GPa for diamond type and range of 6 GPa to 29 GPa for square type. Experimental results showed accurate prediction of compression elastic modulus with average error of 13% for diamond type and 35% for square type respectively. The significance of the methodology and the results showed that different design parameters of the structures can play a major role in the mechanical behaviour of the metallic scaffold.

Rigid tumors contain soft cancer cells

Palpation, as already mentioned in the ancient Egyptian medical text Ebers Papyrus, utilizes that solid tumors are stiffer than the surrounding tissue. However, cancer cell lines tend to soften, which may intuitively foster invasion by enhancing the ability of cancer cells to squeeze through dense tissue. This paradox raises questions besides the oxymoron itself: Does softness emerge from adaptation to the external microenvironment? Or are soft cells already present inside a rigid primary tumor mass to support cancer cell unjamming? We investigate primary tumor explants from patients with breast and cervix carcinomas on multiple length scales from the tissue level down to single cells. We find that primary tumors are highly heterogeneous in their mechanical properties. From the tissue level this heterogeneity persists down to the scale of individual cells in cancer cell clusters, resulting in a broad distribution of cell rigidities with a higher fraction of softer, more squeezable cells. Plus, squeezed cell shapes correlate with cancer cell motility. Mechanical modelling based on patient data reveals that a tumor mass as a whole is able to maintain a rigid, solid behavior even when it contains a significant fraction of very soft cells. Cell softening induced cancer cell unjamming generates heterogeneous cancer cell clusters with a solid backbone of rigid cells surrounded by soft motile cells.

Mitotic Outcomes in Fibrous Environments

During mitosis, cells round up and generate outward forces to create space and orient the mitotic spindles. Here, using suspended ECM-mimicking nanofiber networks, we recapitulate in vivo adhesion organization and confinement to interrogate mitotic outcomes for various interphase cell shapes. Elongated cells attached to single fibers through two focal adhesion clusters (FACs) at their extremities result in perfect spherical mitotic cell bodies that undergo large 3D displacement while being held by retraction fibers. Increasing the number of parallel fibers increases cellular extremity FACs and retraction fiber-driven stability, leading to reduced 3D cell-body movement, metaphase plate rotations, and significantly faster division times. Interestingly, interphase kite shapes on a crosshatch pattern of four fibers undergo mitosis resembling single-fiber outcomes due to rounded bodies being primarily held in position by retraction fibers from two perpendicular suspended fibers. We develop a cortex-astral microtubule analytical friction and force model to capture retraction-fiber-driven stability of the metaphase plate rotations. We report that reduced orientational stability results in increased monopolar mitotic defects. In the case of cells attached to two parallel fibers, rounded mitotic cells can get confined between the suspended fibers, allowing estimation of the mitotic forces through measurement of the outward deflection of the fibers. Interestingly, confinement causes rotated mitotic spindles similar to those reported in dense tissues. Overall, we establish dynamics of mitosis in fibrous environments governed by fiber arrangement and architecture-driven differences in interphase cell shapes, adhesion geometries, and varying levels of mechanical confinement.

Biomechanics of Additively Manufactured Metallic Scaffolds—A Review

This review paper is related to the biomechanics of additively manufactured (AM) metallic scaffolds, in particular titanium alloy Ti6Al4V scaffolds. This is because Ti6Al4V has been identified as an ideal candidate for AM metallic scaffolds. The factors that affect the scaffold technology are the design, the material used to build the scaffold, and the fabrication process. This review paper includes thus a discussion on the design of Ti6A4V scaffolds in relation to how their behavior is affected by their cell shapes and porosities. This is followed by a discussion on the post treatment and mechanical characterization including in-vitro and in-vivo biomechanical studies. A review and discussion are also presented on the ongoing efforts to develop predictive tools to derive the relationships between structure, processing, properties and performance of powder-bed additive manufacturing of metals. This is a challenge when developing process computational models because the problem involves multi-physics and is of multi-scale in nature. Advantages, limitations, and future trends in AM scaffolds are finally discussed. AM is considered at the forefront of Industry 4.0, the fourth industrial revolution. The market of scaffold technology will continue to boom because of the high demand for human tissue repair.

Cluster size distribution of cells disseminating from a primary tumor

The first stage of the metastatic cascade often involves motile cells emerging from a primary tumor either as single cells or as clusters. These cells enter the circulation, transit to other parts of the body and finally are responsible for growth of secondary tumors in distant organs. The mode of dissemination is believed to depend on the EMT nature (epithelial, hybrid or mesenchymal) of the cells. Here, we calculate the cluster size distribution of these migrating cells, using a mechanistic computational model, in presence of different degree of EMT-ness of the cells; EMT is treated as given rise to changes in their active motile forces (μ) and cell-medium surface tension (Γ). We find that, for (μ > μmin, Γ > 1), when the cells are hybrid in nature, the mean cluster size, N ¯ ∼ Γ 2 . 0 / μ 2 . 8, where μmin increases with increase in Γ. For Γ ≤ 0, N ¯ = 1, the cells behave as completely mesenchymal. In presence of spectrum of hybrid states with different degree of EMT-ness (motility) in primary tumor, the cells which are relatively more mesenchymal (higher μ) in nature, form larger clusters, whereas the smaller clusters are relatively more epithelial (lower μ). Moreover, the heterogeneity in μ is comparatively higher for smaller clusters with respect to that for larger clusters. We also observe that more extended cell shapes promote the formation of smaller clusters. Overall, this study establishes a framework which connects the nature and size of migrating clusters disseminating from a primary tumor with the phenotypic composition of the tumor, and can lead to the better understanding of metastasis.