Cell-strain-energy costs of active control of contractility.

Cell mechanosensing is implicated in the control of a broad range of cell behaviors, with cytoskeletal contractility a key component. Experimentally, it is observed that the contractility of the cell responds to increasing substrate stiffness, showing increased contractile force and changing the distribution of cytoskeletal elements. Here, we show using a theoretical model of active cell contractility that upregulation of contractility need not be energetically expensive, especially when combined with changes in adhesion and contractile distribution. Indeed, we show that a feedback mechanism based on the maintenance of strain energy would require an upregulation in contractile pressure on all but the softest substrates. We consider both the commonly reported substrate strain energy and active work done. We demonstrate substrate strain energy would preferentially select for the experimentally observed clustering of cell adhesions on stiffer substrates which effectively soften the substrate and enable an upregulation of total contractile pressure, while the localization of contractility has the greatest impact on the internal work.

Sticking around: Cell adhesion patterning for energy minimization and substrate mechanosensing.

Tissue stiffness (Young's modulus) is a key control parameter in cell behavior and bioengineered gels where defined mechanical properties have become an essential part of the toolkit for interrogating mechanotransduction. Here, we show using a mechanical cell model that the effective substrate stiffness experienced by a cell depends, not just on the engineered mechanical properties of the substrate but critically also on the particular arrangement of adhesions between cell and substrate. In particular, we find that cells with different adhesion patterns can experience two different gel stiffnesses as equivalent and will generate the same mean cell deformations. In considering small patches of adhesion, which mimic focal adhesion complexes, we show how the experimentally observed focal adhesion growth and elongation on stiff substrates can be explained by energy considerations. Relatedly, energy arguments also provide a reason why nascent adhesions do not establish into focal adhesions on soft substrates, as has been commonly observed. Fewer and larger adhesions are predicted to be preferred over more and smaller, an effect enhanced by random spot placing with the simulations predicting qualitatively realistic cell shapes in this case.

A Spatially Resolved Mechanistic Growth Law for Cancer Drug Development Predicting Tumor Growing Fractions.

Mathematical models used in preclinical drug discovery tend to be empirical growth laws. Such models are well suited to fitting the data available, mostly longitudinal studies of tumor volume; however, they typically have little connection with the underlying physiologic processes. This lack of a mechanistic underpinning restricts their flexibility and potentially inhibits their translation across studies including from animal to human. Here we present a mathematical model describing tumor growth for the evaluation of single-agent cytotoxic compounds that is based on mechanistic principles. The model can predict spatial distributions of cell subpopulations and account for spatial drug distribution effects within tumors. Importantly, we demonstrate that the model can be reduced to a growth law similar in form to the ones currently implemented in pharmaceutical drug development for preclinical trials so that it can integrated into the current workflow. We validate this approach for both cell-derived xenograft and patient-derived xenograft (PDX) data. This shows that our theoretical model fits as well as the best performing and most widely used models. However, in addition, the model is also able to accurately predict the observed growing fraction of tumours. Our work opens up current preclinical modeling studies to also incorporating spatially resolved and multimodal data without significant added complexity and creates the opportunity to improve translation and tumor response predictions. Significance: This theoretical model has the same mathematical structure as that currently used for drug development. However, its mechanistic basis enables prediction of growing fraction and spatial variations in drug distribution.

Micromechanical mapping of the intact ovary interior reveals contrasting mechanical roles for follicles and stroma.

Follicle development in the ovary must be tightly regulated to ensure cyclical release of oocytes (ovulation). Disruption of this process is a common cause of infertility, for example via polycystic ovary syndrome (PCOS) and premature ovarian insufficiency (POI). Recent ex vivo studies suggest that follicle growth is mechanically regulated, however, crucially, the actual mechanical properties of the follicle microenvironment have remained unknown. Here we use atomic force microscopy (AFM) spherical probe indentation to map and quantify the mechanical microenvironment in the mouse ovary, at high resolution and across the entire width of the intact (bisected) ovarian interior. Averaging over the entire organ, we find the ovary to be a fairly soft tissue comparable to fat or kidney (mean Young's Modulus 3.3±2.5 kPa). This average, however, conceals substantial spatial variations, with the overall range of tissue stiffnesses from c. 0.5-10 kPa, challenging the concept that a single Young's Modulus can effectively summarize this complex organ. Considering the internal architecture of the ovary, we find that stiffness is low at the edge and centre which are dominated by stromal tissue, and highest in an intermediate zone that is dominated by large developmentally-advanced follicles, confirmed by comparison with immunohistology images. These results suggest that large follicles are mechanically dominant structures in the ovary, contrasting with previous expectations that collagen-rich stroma would dominate. Extending our study to the highest resolutions (c. 5 µm) showed substantial mechanical variations within the larger zones, even over very short (sub-100 µm) lengths, and especially within the stiffer regions of the ovary. Taken together, our results provide a new, physiologically accurate, framework for ovarian biomechanics and follicle tissue engineering.

DEP-Dots for 3D cell culture: low-cost, high-repeatability, effective 3D cell culture in multiple gel systems.

It is known that cells grown in 3D are more tolerant to drug treatment than those grown in dispersion, but the mechanism for this is still not clear; cells grown in 3D have opportunities to develop inter-cell communication, but are also closely packed which may impede diffusion. In this study we examine methods for dielectrophoresis-based cell aggregation of both suspension and adherent cell lines, and compare the effect of various drugs on cells grown in 3D and 2D. Comparing viability of pharmacological interventions on 3D cell clusters against both suspension cells and adherent cells grown in monolayer, as well as against a unicellular organism with no propensity for intracellular communication, we suggest that 3D aggregates of adherent cells, compared to suspension cells, show a substantially different drug response to cells grown in monolayer, which increases as the IC50 is approached. Further, a mathematical model of the system for each agent demonstrates that changes to drug response are due to inherent changes in the system of adherent cells from the 2D to 3D state. Finally, differences in the electrophysiological membrane properties of the adherent cell type suggest this parameter plays an important role in the differences found in the 3D drug response.

Opportunities for Quantitative Translational Modeling in Oncology.

A 2-day meeting was held by members of the UK Quantitative Systems Pharmacology Network () in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.

Nuclear exclusion of SMAD2/3 in granulosa cells is associated with primordial follicle activation in the mouse ovary.

Maintenance and activation of the limited supply of primordial follicles in the ovary are important determinants of reproductive lifespan. Currently, the molecular programme that maintains the primordial phenotype and the early events associated with follicle activation are not well defined. Here, we have systematically analysed these events using microscopy and detailed image analysis. Using the immature mouse ovary as a model, we demonstrate that the onset of granulosa cell (GC) proliferation results in increased packing density on the oocyte surface and consequent GC cuboidalization. These events precede oocyte growth and nuclear translocation of FOXO3a, a transcription factor important in follicle activation. Immunolabelling of the TGFß signalling mediators and transcription factors SMAD2/3 revealed a striking expression pattern specific to GCs of small follicles. SMAD2/3 were expressed in the nuclei of primordial GCs but were mostly excluded in early growing follicles. In activated follicles, GC nuclei lacking SMAD2/3 generally expressed Ki67. These findings suggest that the first phenotypic changes during follicle activation are observed in GCs, and that TGFß signalling is fundamental for regulating GC arrest and the onset of proliferation.

A computational model of nuclear self-organisation in syncytial embryos.

Syncytial embryos develop through cycles of nuclear division and rearrangement within a common cytoplasm. A paradigm example is Drosophila melanogaster in which nuclei form an ordered array in the embryo surface over cell cycles 10-13. This ordering process is assumed to be essential for subsequent cellularisation. Using quantitative tissue analysis, it has previously been shown that the regrowth of actin and microtubule networks after nuclear division generates reordering forces that counteract its disordering effect (Kanesaki et al., 2011). We present here an individual-based computer simulation modelling the nuclear dynamics. In contrast to similar modelling approaches e.g. epithelial monolayers or tumour spheroids, we focus not on the spatial dependence, but rather on the time-dependence of the interaction laws. We show that appropriate phenomenological inter-nuclear force laws reproduce the experimentally observed dynamics provided that the cytoskeletal network regrows sufficiently quickly after mitosis. Then repulsive forces provided by the actin system are necessary and sufficient to regain the observed level of order in the system, after the strong disruption resulting from cytoskeletal network disassembly and spindle formation. We also observe little mixing of nuclei through cell cycles. Our study highlights the importance of the dynamics of cytoskeletal forces during this critical phase of syncytial development and emphasises the need for real-time experimental data at high temporal resolution.

Developmental biology: a growing role for computer simulations.

Keeping cells separated in well-defined domains is essential for development. A new computational-experimental study elucidates the physical mechanisms that establish and maintain the dorsal-ventral compartment boundary in the Drosophila wing disc and demonstrates the increasing value of computer simulations in developmental biology.