Tissue-scale Dynamics Research Articles

A time- and single-cell-resolved model of murine bone marrow hematopoiesis

The paradigmatic hematopoietic tree model is increasingly recognized to be limited, as it is based on heterogeneous populations largely defined by non-homeostatic assays testing cell fate potentials. Here, we combine persistent labeling with time-series single-cell RNA sequencing to build a real-time, quantitative model of invivo tissue dynamics for murine bone marrow hematopoiesis. We couple cascading single-cell expression patterns with dynamic changes in differentiation and growth speeds. The resulting explicit linkage between molecular states and cellular behavior reveals widely varying self-renewal and differentiation properties across distinct lineages. Transplanted stem cells show strong acceleration of differentiation at specific stages of erythroid and neutrophil production, illustrating how the model can quantify the impact of perturbations. Our reconstruction of dynamic behavior from snapshot measurements is akin to how a kinetoscope allows sequential images to merge into a movie. We posit that this approach is generally applicable to understanding tissue-scale dynamics at high resolution.

Shape-driven confluent rigidity transition in curved biological tissues

Collective cell motions underlie structure formation during embryonic development. Tissues exhibit emergent multicellular characteristics such as jamming, rigidity transitions, and glassy dynamics, but there remain questions about how those tissue-scale dynamics derive from local cell-level properties. Specifically, there has been little consideration of the interplay between local tissue geometry and cellular properties influencing larger-scale tissue behaviors. Here, we consider a simple two-dimensional computational vertex model for confluent tissue monolayers, which exhibits a rigidity phase transition controlled by the shape index (ratio of perimeter to square root area) of cells, on surfaces of constant curvature. We show that the critical point for the rigidity transition is a function of curvature such that positively curved systems are likely to be in a less rigid, more fluid, phase. Likewise, negatively curved systems (saddles) are likely to be in a more rigid, less fluid, phase. A phase diagram we generate for the curvature and shape index constitutes a testable prediction from the model. The curvature dependence is interesting because it suggests a natural explanation for more dynamic tissue remodeling and facile growth in regions of higher surface curvature. Conversely, we would predict stability at the base of saddle-shaped budding structures without invoking the need for biochemical or other physical differences. This concept has potential ramifications for our understanding of morphogenesis of budding and branching structures.

Exploring Lignification Complexity in Plant Cell Walls with Airyscan Super-resolution Microscopy and Bioorthogonal Chemistry.

In this paper, we present the use of multiplex click/bioorthogonal chemistry combined with super-resolution Airyscan microscopy to track biomolecules in living systems with a focus on studying lignin formation in plant cell walls. While laser scanning confocal microscopy (LSCM) provided insights into the tissue-scale dynamics of lignin formation and distribution in our previous reports, its limited resolution precluded an in-depth analysis of lignin composition at the unique cell wall or substructure level. To overcome this limitation, we explored the use of Airyscan microscopy, which, among the super-resolution techniques available, offers an optimal balance between performance, cost, accessibility, and ease of implementation. Our study demonstrates that a triple labeling strategy using copper-catalyzed azide-alkyne cycloaddition (CuAAC), strain-promoted azide-alkyne cycloaddition (SPAAC), and inverse electronic-demand Diels-Alder cycloaddition (IEDDA) to label modified lignin metabolic precursors can be combined with Airyscan microscopy to reveal the zones of active lignification at the single cell level with improved sensitivity and resolution. This approach enables insights into the lignin composition in wall substructures, such as pits or in wall layers that are otherwise not distinguishable by classical LSCM. Our work emphasizes the importance of studying lignin formation in plant cell walls and demonstrates the potential of combining bioorthogonal chemistry and super-resolution microscopy techniques for studying biomolecules in living systems.

Laser Ablation and Intravital Microscopy to Study Intestinal Remodeling.

Investigating intestinal recovery in vivo is an exquisite technical challenge. A lack of longitudinal imaging protocols has prevented deeper insights into the cell and tissue scale dynamics that orchestrate intestinal regeneration. Here, we describe an intravital microscopy method that locally induces tissue damage at the single crypt scale and follows the regenerative response of the intestinal epithelium in living mice. Single crypts or larger intestinal fields were ablated by a high-intensity multiphoton infrared laser in a time- and space-controlled manner. Subsequent long-term repetitive intravital imaging enabled the tracking of the damaged areas over time and allowed for the monitoring of crypt dynamics during tissue recovery over a period of multiple weeks. Crypt remodeling events such as crypt fission, fusion, and disappearance were observed in the neighboring tissue upon laser-induced damage. This protocol enables the study of crypt dynamics both in homeostatic and pathophysiological settings, such as aging and tumor initiation.

Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment

We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. Our work aims to decrease both the time and cost required to develop nanoparticle designs. EVONANO includes a simulator to grow tumours, extract representative scenarios, and simulate nanoparticle transport through these scenarios in order to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments. Our platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.

Multiscale dynamics of tight junction remodeling.

Epithelial cells form tissues that generate biological barriers in the body. Tight junctions (TJs) are responsible for maintaining a selectively permeable seal between epithelial cells, but little is known about how TJs dynamically remodel in response to physiological forces that challenge epithelial barrier function, such as cell shape changes (e.g. during cell division) or tissue stretching (e.g. during developmental morphogenesis). In this Review, we first introduce a framework to think about TJ remodeling across multiple scales: from molecular dynamics, to strand dynamics, to cell- and tissue-scale dynamics. We then relate knowledge gained from global perturbations of TJs to emerging information about local TJ remodeling events, where transient localized Rho activation and actomyosin-mediated contraction promote TJ remodeling to repair local leaks in barrier function. We conclude by identifying emerging areas in the field and propose ideas for future studies that address unanswered questions about the mechanisms that drive TJ remodeling.

PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems.

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal “virtual laboratory” for such multicellular systems simulates both the biochemical microenvironment (the “stage”) and many mechanically and biochemically interacting cells (the “players” upon the stage). PhysiCell—physics-based multicellular simulator—is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility “out of the box.” The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a “cellular cargo delivery” system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.

Force-dependent binding of vinculin to α-catenin regulates cell-cell contact stability and collective cell behavior.

The shaping of a multicellular body and repair of adult tissues require fine--tuning of cell adhesion, cell mechanics, and intercellular transmission of mechanical load. Adherens junctions (AJs) are the major intercellular junctions by which cells sense and exert mechanical force on each other. However, how AJs adapt to mechanical stress and how this adaptation contributes to cell-cell cohesion and eventually to tissue-scale dynamics and mechanics remains largely unknown. Here, by analyzing the tension-dependent recruitment of vinculin, α-catenin, and F-actin as a function of stiffness, as well as the dynamics of GFP-tagged wild-type and mutated α-catenins, altered for their binding capability to vinculin, we demonstrate that the force-dependent binding of vinculin stabilizes α-catenin and is responsible for AJ adaptation to force. Challenging cadherin complexes mechanical coupling with magnetic tweezers, and cell-cell cohesion during collective cell movements, further highlight that tension-dependent adaptation of AJs regulates cell-cell contact dynamics and coordinated collective cell migration. Altogether, these data demonstrate that the force-dependent α-catenin/vinculin interaction, manipulated here by mutagenesis and mechanical control, is a core regulator of AJ mechanics and long-range cell-cell interactions.

Unified quantitative characterization of epithelial tissue development.

Understanding the mechanisms regulating development requires a quantitative characterization of cell divisions, rearrangements, cell size and shape changes, and apoptoses. We developed a multiscale formalism that relates the characterizations of each cell process to tissue growth and morphogenesis. Having validated the formalism on computer simulations, we quantified separately all morphogenetic events in the Drosophila dorsal thorax and wing pupal epithelia to obtain comprehensive statistical maps linking cell and tissue scale dynamics. While globally cell shape changes, rearrangements and divisions all significantly participate in tissue morphogenesis, locally, their relative participations display major variations in space and time. By blocking division we analyzed the impact of division on rearrangements, cell shape changes and tissue morphogenesis. Finally, by combining the formalism with mechanical stress measurement, we evidenced unexpected interplays between patterns of tissue elongation, cell division and stress. Our formalism provides a novel and rigorous approach to uncover mechanisms governing tissue development.