Memory of cell shape biases stochastic fate decision-making despite mitotic rounding

Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning

Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised machine learning, requiring the extraction of features that not only faithfully preserve this heterogeneity but also effectively discriminate between established biological states, all while remaining interpretable. To tackle these challenges, a self-training deep learning framework designed for fine-grained and interpretable phenotyping is presented. This framework incorporates an unsupervised teacher model with interpretable features to facilitate feature learning in a student deep neural network (DNN). Significantly, an autoencoder-based regularizer is designed to encourage the student DNN to maximize the heterogeneity associated with molecular perturbations. This method enables the acquisition of features with enhanced discriminatory power, while simultaneously preserving the heterogeneity associated with molecular perturbations. This study successfully delineated fine-grained phenotypes within the heterogeneous protrusion dynamics of migrating epithelial cells, revealing specific responses to pharmacological perturbations. Remarkably, this framework adeptly captured a concise set of highly interpretable features uniquely linked to these fine-grained phenotypes, each corresponding to specific temporal intervals crucial for their manifestation. This unique capability establishes it as a valuable tool for investigating diverse cellular dynamics and their heterogeneity.

Lifelike Transformative Materials for Biohybrid Implants: Inspired by Nature, Driven by Technology

Today's living world is enriched with a myriad of natural biological designs, shaped by billions of years of evolution. Unraveling the construction rules of living organisms offers the potential to create new materials and systems for biomedicine. From the close examination of living organisms, several concepts emerge: hierarchy, pattern repetition, adaptation, and irreducible complexity. All these aspects must be tackled to develop transformative materials with lifelike behavior. This perspective article highlights recent progress in the development of transformative biohybrid systems for applications in the fields of tissue regeneration and biomedicine. Advances in computational simulations and data-driven predictions are also discussed. These tools enable the virtual high-throughput screening of implant design and performance before committing to fabrication, thus reducing the development time and cost of biomimetic and biohybrid constructs. The ongoing progress of imaging methods also constitutes an essential part of this matter in order to validate the computation models and enable longitudinal monitoring. Finally, the current challenges of lifelike biohybrid materials, including reproducibility, ethical considerations, and translation, are discussed. Advances in the development of lifelike materials will open new biomedical horizons, where perhaps what is currently envisioned as science fiction will become a science-driven reality in the future.

Sandwich Biomimetic Scaffold Based Tendon Stem/Progenitor Cell Alignment in a 3D Microenvironment for Functional Tendon Regeneration

Tendon injuries are some of the most commonly diagnosed musculoskeletal diseases. Tendon regeneration is sensitive to the topology of the substitute as it affects the cellular microenvironment and homeostasis. To bionic in vivo three-dimensional (3D) aligned microenvironment, an ordered 3D sandwich model was used to investigate the cell response in the tendon. First, high-resolution 3D printing provided parallel-grooved topographical cues on the hydrogel surface. Then the cells were seeded on its surface to acquire a 2D model. Afterward, an additional hydrogel coating layer was applied to the cells to create the 3D model. The interaction between cells and order structures in three-dimensions is yet to be explored. The study found that the tendon stem/progenitor cells (TSPCs) still maintain their ordering growth in the 3D model as in the 2D model. The study also found that the 3D-aligned TSPCs exhibited enhanced tenogenic differentiation through the PI3K-AKT signaling pathway and presented a less inflammatory phenotype than those in the 2D model. The in vivo implantation of such a 3D-aligned TSPC composite promoted tendon regeneration and mitigated heterotopic ossification in an Achilles defect model. These findings demonstrated that 3D-aligned TSPCs within a biomimetic topology environment are promising for functional tendon regeneration.

Geometric Constraints Regulate Energy Metabolism and Cellular Contractility in Vascular Smooth Muscle Cells by Coordinating Mitochondrial DNA Methylation

Vascular smooth muscle cells (SMCs) can adapt to changes in cellular geometric cues; however, the underlying mechanisms remain elusive. Using 2D micropatterned substrates to engineer cell geometry, it is found that in comparison with an elongated geometry, a square-shaped geometry causes the nuclear-to-cytoplasmic redistribution of DNA methyltransferase 1 (DNMT1), hypermethylation of mitochondrial DNA (mtDNA), repression of mtDNA gene transcription, and impairment of mitochondrial function. Using irregularly arranged versus circumferentially aligned vascular grafts to control cell geometry in 3D growth, it is demonstrated that cell geometry, mtDNA methylation, and vessel contractility are closely related. DNMT1 redistribution is found to be dependent on the phosphoinositide 3-kinase and protein kinase B (AKT) signaling pathways. Cell elongation activates cytosolic phospholipase A2, a nuclear mechanosensor that, when inhibited, hinders AKT phosphorylation, DNMT1 nuclear accumulation, and energy production. The findings of this study provide insights into the effects of cell geometry on SMC function and its potential implications in the optimization of vascular grafts.

Implementing computational modeling in tissue engineering: where disciplines meet

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.

Mechano-regulated cell-cell signaling in the context of cardiovascular tissue engineering

Cardiovascular tissue engineering (CVTE) aims to create living tissues, with the ability to grow and remodel, as replacements for diseased blood vessels and heart valves. Despite promising results, the (long-term) functionality of these engineered tissues still needs improvement to reach broad clinical application. The functionality of native tissues is ensured by their specific mechanical properties directly arising from tissue organization. We therefore hypothesize that establishing a native-like tissue organization is vital to overcome the limitations of current CVTE approaches. To achieve this aim, a better understanding of the growth and remodeling (G&R) mechanisms of cardiovascular tissues is necessary. Cells are the main mediators of tissue G&R, and their behavior is strongly influenced by both mechanical stimuli and cell-cell signaling. An increasing number of signaling pathways has also been identified as mechanosensitive. As such, they may have a key underlying role in regulating the G&R of tissues in response to mechanical stimuli. A more detailed understanding of mechano-regulated cell-cell signaling may thus be crucial to advance CVTE, as it could inspire new methods to control tissue G&R and improve the organization and functionality of engineered tissues, thereby accelerating clinical translation. In this review, we discuss the organization and biomechanics of native cardiovascular tissues; recent CVTE studies emphasizing the obtained engineered tissue organization; and the interplay between mechanical stimuli, cell behavior, and cell-cell signaling. In addition, we review past contributions of computational models in understanding and predicting mechano-regulated tissue G&R and cell-cell signaling to highlight their potential role in future CVTE strategies.

Role of Cell Morphology in Classical Delta-Notch Pattern Formation

Notch signaling is responsible for creating contrasting states of differentiation among neighboring cells during organism's early development. Various factors can affect this highly conserved intercellular signaling pathway, for the formation of fine-grained pattern in cell tissues. As cells undergo dramatic structural changes during development, one of the factors that can influence cell-cell communication is cell morphology. In this study, we elucidate the role of cell morphology on mosaic pattern formation in a realistic epithelial layer cell model. We discovered that cell signaling strength is inversely related to the cell area, such that smaller cells have higher probability/tendency of becoming signal producing cells as compared to larger cells during early embryonic days. In a nutshell, our work highlights the role of cell morphology on the stochastic cell fate decision process in the epithelial layer of multicellular organisms.

Emerging machine learning approaches to phenotyping cellular motility and morphodynamics

Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

The Mechanics of Mitotic Cell Rounding

When animal cells enter mitosis, they round up to become spherical. This shape change is accompanied by changes in mechanical properties. Multiple studies using different measurement methods have revealed that cell surface tension, intracellular pressure and cortical stiffness increase upon entry into mitosis. These cell-scale, biophysical changes are driven by alterations in the composition and architecture of the contractile acto-myosin cortex together with osmotic swelling and enable a mitotic cell to exert force against the environment. When the ability of cells to round is limited, for example by physical confinement, cells suffer severe defects in spindle assembly and cell division. The requirement to push against the environment to create space for spindle formation is especially important for cells dividing in tissues. Here we summarize the evidence and the tools used to show that cells exert rounding forces in mitosis in vitro and in vivo, review the molecular basis for this force generation and discuss its function for ensuring successful cell division in single cells and for cells dividing in normal or diseased tissues.

Prediction and quantification of bacterial biofilm detachment using Glazier-Graner-Hogeweg method based model simulations

Morphological changes in bacterial biofilm structures arise from the fluid-structure interactions between the biofilm and the surrounding fluid. Depending on the magnitude of the force acting on the structure, the bacteria rearrange to attain an equilibrium shape or get washed away by the moving fluid. Understanding the dynamics behind the evolution of such equilibrium or failed states can aid in development of tools for biofilm removal or eradication. We develop a Glazier-Graner-Hogeweg method-based model to explore the collective evolution of biofilm morphology arising from cell-cell and cell-fluid interactions. We show that low adherence and high motility of the cells leads to sloughing of biofilms. Also, streamers are found to form under laminar flow conditions in tightly packed biofilms. In mixed species biofilms, we found that a species with less cell-cell binding affinity gets eroded faster than its counterpart. Therefore, we hypothesize that in nature these less-adherent species should be present encapsulated within the biofilm structure to maximize their chances of survival.

Predicting the future direction of cell movement with convolutional neural networks

Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell movement. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future direction of cell movement from current cell shape, and can be used to automatically identify those morphological features that influence future cell movement.

Zebrafish Klf4 maintains the ionocyte progenitor population by regulating epidermal stem cell proliferation and lateral inhibition

In the skin and gill epidermis of fish, ionocytes develop alongside keratinocytes and maintain body fluid ionic homeostasis that is essential for adaptation to environmental fluctuations. It is known that ionocyte progenitors in zebrafish embryos are specified from p63+ epidermal stem cells through a patterning process involving DeltaC (Dlc)-Notch-mediated lateral inhibition, which selects scattered dlc+ cells into the ionocyte progenitor fate. However, mechanisms by which the ionocyte progenitor population is modulated remain unclear. Krüppel-like factor 4 (Klf4) transcription factor was previously implicated in the terminal differentiation of mammalian skin epidermis and is known for its bifunctional regulation of cell proliferation in a tissue context-dependent manner. Here, we report novel roles for zebrafish Klf4 in the ventral ectoderm during embryonic skin development. We found that Klf4 was expressed in p63+ epidermal stem cells of the ventral ectoderm from 90% epiboly onward. Knockdown or knockout of klf4 expression reduced the proliferation rate of p63+ stem cells, resulting in decreased numbers of p63+ stem cells, dlc-p63+ keratinocyte progenitors and dlc+ p63+ ionocyte progenitor cells. These reductions subsequently led to diminished keratinocyte and ionocyte densities and resulted from upregulation of the well-known cell cycle regulators, p53 and cdkn1a/p21. Moreover, mutation analyses of the KLF motif in the dlc promoter, combined with VP16-klf4 or engrailed-klf4 mRNA overexpression analyses, showed that Klf4 can bind the dlc promoter and modulate lateral inhibition by directly repressing dlc expression. This idea was further supported by observing the lateral inhibition outcomes in klf4-overexpressing or knockdown embryos. Overall, our experiments delineate novel roles for zebrafish Klf4 in regulating the ionocyte progenitor population throughout early stem cell stage to initiation of terminal differentiation, which is dependent on Dlc-Notch-mediated lateral inhibition.

Reticular adhesions are a distinct class of cell-matrix adhesions that mediate attachment during mitosis

Adhesion to the extracellular matrix persists during mitosis in most cell types. However, while classical adhesion complexes, such as focal adhesions, do and must disassemble to enable mitotic rounding, the mechanisms of residual mitotic cell-extracellular matrix adhesion remain undefined. Here, we identify 'reticular adhesions', a class of adhesion complex that is mediated by integrin αvβ5, formed during interphase, and preserved at cell-extracellular matrix attachment sites throughout cell division. Consistent with this role, integrin β5 depletion perturbs mitosis and disrupts spatial memory transmission between cell generations. Reticular adhesions are morphologically and dynamically distinct from classical focal adhesions. Mass spectrometry defines their unique composition, enriched in phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P2)-binding proteins but lacking virtually all consensus adhesome components. Indeed, reticular adhesions are promoted by PtdIns(4,5)P2, and form independently of talin and F-actin. The distinct characteristics of reticular adhesions provide a solution to the problem of maintaining cell-extracellular matrix attachment during mitotic rounding and division.

Modeling the Notch Response

NOTCH signaling regulates developmental processes in all tissues and all organisms across the animal kingdom. It is often involved in coordinating the differentiation of neighboring cells into different cell types. As our knowledge on the structural, molecular and cellular properties of the NOTCH pathway expands, there is a greater need for quantitative methodologies to get a better understanding of the processes controlled by NOTCH signaling. In recent years, theoretical and computational approaches to NOTCH signaling and NOTCH mediated patterning are gaining popularity. Mathematical models of NOTCH mediated patterning provide insight into complex and counterintuitive behaviors and can help generate predictions that can guide experiments. In this chapter, we review the recent advances in modeling NOTCH mediated patterning processes. We discuss new modeling approaches to lateral inhibition patterning that take into account cis-interactions between NOTCH receptors and ligands, signaling through long cellular protrusions, cell division processes, and coupling to external signals. We also describe models of somitogenesis, where NOTCH signaling is used for synchronizing cellular oscillations. We then discuss modeling approaches that consider the effect of cell morphology on NOTCH signaling and NOTCH mediated patterning. Finally, we consider models of boundary formation and how they are influenced by the combinatorial action of multiple ligands. Together, these topics cover the main advances in the field of modeling the NOTCH response.

Modeling mechanical inhomogeneities in small populations of proliferating monolayers and spheroids

Understanding the mechanical behavior of multicellular monolayers and spheroids is fundamental to tissue culture, organism development, and the early stages of tumor growth. Proliferating cells in monolayers and spheroids experience mechanical forces as they grow and divide and local inhomogeneities in the mechanical microenvironment can cause individual cells within the multicellular system to grow and divide at different rates. This differential growth, combined with cell division and reorganization, leads to residual stress. Multiple different modeling approaches have been taken to understand and predict the residual stresses that arise in growing multicellular systems, particularly tumor spheroids. Here, we show that by using a mechanically robust agent-based model constructed with the peridynamic framework, we gain a better understanding of residual stresses in multicellular systems as they grow from a single cell. In particular, we focus on small populations of cells (1-100 s) where population behavior is highly stochastic and prior investigation has been limited. We compare the average strain energy density of cells in monolayers and spheroids using different growth and division rules and find that, on average, cells in spheroids have a higher strain energy density than cells in monolayers. We also find that cells in the interior of a growing spheroid are, on average, in compression. Finally, we demonstrate the importance of accounting for stochastic fluctuations in the mechanical environment, particularly when the cellular response to mechanical cues is nonlinear. The results presented here serve as a starting point for both further investigation with agent-based models, and for the incorporation of major findings from agent-based models into continuum scale models when explicit representation of individual cells is not computationally feasible.

Physiological effects of a novel artificially synthesized antimalarial cyclic peptide: Mahafacyclin B

Mahafacyclin B is a cyclic peptide isolated from the latex of Jatropha mahafalensis and is an antimalarial agent. However, the physiological effects of mahafacyclin B in mammalian cells are not known. Here, we assessed the growth, morphology, and alterations in the transcriptome of CHO-K1 cells exposed to mahafacyclin B (0-22 μM). Mahafacyclin B at 2.2 μM did not affect the proliferation or death of CHO-K1 cells. Mahafacyclin B was not toxic to mammalian cells at 2.2 μM, which represents a normal physiological concentration at which mahafacyclin B retains its antimalarial properties. Interestingly, mahafacyclin B altered the size and morphology of CHO-K1 cells. Comparative transcriptomics revealed that mahafacyclin B modulated the expression of a specific subset of genes.

The temporal basis of angiogenesis

The process of new blood vessel growth (angiogenesis) is highly dynamic, involving complex coordination of multiple cell types. Though the process must carefully unfold over time to generate functional, well-adapted branching networks, we seldom hear about the time-based properties of angiogenesis, despite timing being central to other areas of biology. Here, we present a novel, time-based formulation of endothelial cell behaviour during angiogenesis and discuss a flurry of our recent, integrated in silico/in vivo studies, put in context to the wider literature, which demonstrate that tissue conditions can locally adapt the timing of collective cell behaviours/decisions to grow different vascular network architectures. A growing array of seemingly unrelated ‘temporal regulators’ have recently been uncovered, including tissue derived factors (e.g. semaphorins or the high levels of VEGF found in cancer) and cellular processes (e.g. asymmetric cell division or filopodia extension) that act to alter the speed of cellular decisions to migrate. We will argue that ‘temporal adaptation’ provides a novel account of organ/disease-specific vascular morphology and reveals ‘timing’ as a new target for therapeutics. We therefore propose and explain a conceptual shift towards a ‘temporal adaptation’ perspective in vascular biology, and indeed other areas of biology where timing remains elusive.

This article is part of the themed issue ‘Systems morphodynamics: understanding the development of tissue hardware’.

The Canonical Notch Signaling Pathway: Structural and Biochemical Insights into Shape, Sugar, and Force

The Notch signaling pathway relies on a proteolytic cascade to release its transcriptionally active intracellular domain, on force to unfold a protective domain and permit proteolysis, on extracellular domain glycosylation to tune the forces exerted by endocytosed ligands, and on a motley crew of nuclear proteins, chromatin modifiers, ubiquitin ligases, and a few kinases to regulate activity and half-life. Herein we provide a review of recent molecular insights into how Notch signals are triggered and how cell shape affects these events, and we use the new insights to illuminate a few perplexing observations.

Virtual cardiac monolayers for electrical wave propagation

The complex structure of cardiac tissue is considered to be one of the main determinants of an arrhythmogenic substrate. This study is aimed at developing the first mathematical model to describe the formation of cardiac tissue, using a joint in silico-in vitro approach. First, we performed experiments under various conditions to carefully characterise the morphology of cardiac tissue in a culture of neonatal rat ventricular cells. We considered two cell types, namely, cardiomyocytes and fibroblasts. Next, we proposed a mathematical model, based on the Glazier-Graner-Hogeweg model, which is widely used in tissue growth studies. The resultant tissue morphology was coupled to the detailed electrophysiological Korhonen-Majumder model for neonatal rat ventricular cardiomyocytes, in order to study wave propagation. The simulated waves had the same anisotropy ratio and wavefront complexity as those in the experiment. Thus, we conclude that our approach allows us to reproduce the morphological and physiological properties of cardiac tissue.

Cell-Cell Contact Area Affects Notch Signaling and Notch-Dependent Patterning

During development, cells undergo dramatic changes in their morphology. By affecting contact geometry, these morphological changes could influence cellular communication. However, it has remained unclear whether and how signaling depends on contact geometry. This question is particularly relevant for Notch signaling, which coordinates neighboring cell fates through direct cell-cell signaling. Using micropatterning with a receptor trans-endocytosis assay, we show that signaling between pairs of cells correlates with their contact area. This relationship extends across contact diameters ranging from microns to tens of microns. Mathematical modeling predicts that dependence of signaling on contact area can bias cellular differentiation in Notch-mediated lateral inhibition processes, such that smaller cells are more likely to differentiate into signal-producing cells. Consistent with this prediction, analysis of developing chick inner ear revealed that ligand-producing hair cell precursors have smaller apical footprints than non-hair cells. Together, these results highlight the influence of cell morphology on fate determination processes.

Real-time prediction of cell division timing in developing zebrafish embryo

Combination of live-imaging and live-manipulation of developing embryos in vivo provides a useful tool to study developmental processes. Identification and selection of target cells for an in vivo live-manipulation are generally performed by experience- and knowledge-based decision-making of the observer. Computer-assisted live-prediction method would be an additional approach to facilitate the identification and selection of the appropriate target cells. Herein we report such a method using developing zebrafish embryos. We choose V2 neural progenitor cells in developing zebrafish embryo as their successive shape changes can be visualized in real-time in vivo. We developed a relatively simple mathematical method of describing cellular geometry of V2 cells to predict cell division-timing based on their successively changing shapes in vivo. Using quantitatively measured 4D live-imaging data, features of V2 cell-shape at each time point prior to division were extracted and a statistical model capturing the successive changes of the V2 cell-shape was developed. By applying sequential Bayesian inference method to the model, we successfully predicted division-timing of randomly selected individual V2 cells while the cell behavior was being live-imaged. This system could assist pre-selecting target cells desirable for real-time manipulation–thus, presenting a new opportunity for in vivo experimental systems.