Plasticity in the macromolecular-scale causal networks of cell migration

Deep Convolutional and Recurrent Neural Networks for Cell Motility Discrimination and Prediction

Cells in culture display diverse motility behaviors that may reflect differences in cell state and function, providing motivation to discriminate between different motility behaviors. Current methods to do so rely upon manual feature engineering. However, the types of features necessary to distinguish between motility behaviors can vary greatly depending on the biological context, and it is not always clear which features may be most predictive in each setting for distinguishing particular cell types or disease states. Convolutional neural networks (CNNs) are machine learning models allowing for relevant features to be learned directly from spatial data. Similarly, recurrent neural networks (RNNs) are a class of models capable of learning long term temporal dependencies. Given that cell motility is inherently spacio-temporal data, we present an approach utilizing both convolutional and long- short-term memory (LSTM) recurrent neural network units to analyze cell motility data. These RNN models provide accurate classification of simulated motility and experimentally measured motility from multiple cell types, comparable to results achieved with hand-engineered features. The variety of cell motility differences we can detect suggests that the algorithm is generally applicable to additional cell types not analyzed here. RNN autoencoders based on the same architecture are capable of learning motility features in an unsupervised manner and capturing variation between myogenic cells in the latent space. Adapting these RNN models to motility prediction, RNNs are capable of predicting muscle stem cell motility from past tracking data with performance superior to standard motion prediction models. This advance in cell motility prediction may be of practical utility in cell tracking applications.

Community standards for open cell migration data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.

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.

Neuronal Growth Cone Size-Dependent and -Independent Parameters of Microtubule Polymerization

Migration and pathfinding of neuronal growth cones during neurite extension is critically dependent on dynamic microtubules. In this study we sought to determine, which aspects of microtubule polymerization relate to growth cone morphology and migratory characteristics. We conducted a multiscale quantitative microscopy analysis using automated tracking of microtubule plus ends in migrating growth cones of cultured murine dorsal root ganglion (DRG) neurons. Notably, this comprehensive analysis failed to identify any changes in microtubule polymerization parameters that were specifically associated with spontaneous extension vs. retraction of growth cones. This suggests that microtubule dynamicity is a basic mechanism that does not determine the polarity of growth cone response but can be exploited to accommodate diverse growth cone behaviors. At the same time, we found a correlation between growth cone size and basic parameters of microtubule polymerization including the density of growing microtubule plus ends and rate and duration of microtubule growth. A similar correlation was observed in growth cones of neurons lacking the microtubule-associated protein MAP1B. However, MAP1B-null growth cones, which are deficient in growth cone migration and steering, displayed an overall reduction in microtubule dynamicity. Our results highlight the importance of taking growth cone size into account when evaluating the influence on growth cone microtubule dynamics of different substrata, guidance factors or genetic manipulations which all can change growth cone morphology and size. The type of large scale multiparametric analysis performed here can help to separate direct effects that these perturbations might have on microtubule dynamics from indirect effects resulting from perturbation-induced changes in growth cone size.

Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance

Cell populations display heterogeneous and dynamic phenotypic states at multiple scales. Similar to molecular features commonly used to explore cell heterogeneity, cell behavior is a rich phenotypic space that may allow for identification of relevant cell states. Inference of cell state from cell behavior across a time course may enable the investigation of dynamics of transitions between heterogeneous cell states, a task difficult to perform with destructive molecular observations. Cell motility is one such easily observed cell behavior with known biomedical relevance. To investigate heterogenous cell states and their dynamics through the lens of cell behavior, we developed Heteromotility, a software tool to extract quantitative motility features from timelapse cell images. In mouse embryonic fibroblasts (MEFs), myoblasts, and muscle stem cells (MuSCs), Heteromotility analysis identifies multiple motility phenotypes within the population. In all three systems, the motility state identity of individual cells is dynamic. Quantification of state transitions reveals that MuSCs undergoing activation transition through progressive motility states toward the myoblast phenotype. Transition rates during MuSC activation suggest non-linear kinetics. By probability flux analysis, we find that this MuSC motility state system breaks detailed balance, while the MEF and myoblast systems do not. Balanced behavior state transitions can be captured by equilibrium formalisms, while unbalanced switching between states violates equilibrium conditions and would require an external driving force. Our data indicate that the system regulating cell behavior can be decomposed into a set of attractor states which depend on the identity of the cell, together with a set of transitions between states. These results support a conceptual view of cell populations as dynamical systems, responding to inputs from signaling pathways and generating outputs in the form of state transitions and observable motile behaviors.

EZH2 promotes neoplastic transformation through VAV interaction-dependent extranuclear mechanisms

Recently, we reported that the histone methyltransferase, EZH2, controls leukocyte migration through interaction with the cytoskeleton remodeling effector, VAV, and direct methylation of the cytoskeletal regulatory protein, Talin. However, it is unclear whether this extranuclear, epigenetic-independent function of EZH2 has a profound impact on the initiation of cellular transformation and metastasis. Here, we show that EZH2 increases Talin1 methylation and cleavage, thereby enhancing adhesion turnover and promoting accelerated tumorigenesis. This transforming capacity is abolished by targeted disruption of EZH2 interaction with VAV. Furthermore, our studies demonstrate that EZH2 in the cytoplasm is closely associated with cancer stem cell properties, and that overexpression of EZH2, a mutant EZH2 lacking its nuclear localization signal (EZH2ΔNLS), or a methyl-mimicking Talin1 mutant substantially promotes JAK2-dependent STAT3 activation and cellular transformation. Taken together, our results suggest a critical role for the VAV interaction-dependent, extranuclear action of EZH2 in neoplastic transformation.

Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development

Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, embryogenesis, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Known for its social developmental cycle, the bacterium Myxococcus xanthus uses coordinated movement to generate three-dimensional aggregates called fruiting bodies. Despite extensive progress in identifying genes controlling fruiting body development, cell behaviors and cell-cell communication mechanisms that mediate aggregation are largely unknown. We developed an approach to examine emergent behaviors that couples fluorescent cell tracking with data-driven models. A unique feature of this approach is the ability to identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms. The fluorescent cell tracking revealed large deviations in the behavior of individual cells. Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics. Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms. The resulting approach of directly combining behavior quantification with data-driven simulations can be applied to more complex systems of collective cell movement without prior knowledge of the cellular machinery and behavioral cues.

Cellular adhesome screen identifies critical modulators of focal adhesion dynamics, cellular traction forces and cell migration behaviour

Cancer cells migrate from the primary tumour into surrounding tissue in order to form metastasis. Cell migration is a highly complex process, which requires continuous remodelling and re-organization of the cytoskeleton and cell-matrix adhesions. Here, we aimed to identify genes controlling aspects of tumour cell migration, including the dynamic organization of cell-matrix adhesions and cellular traction forces. In a siRNA screen targeting most cell adhesion-related genes we identified 200+ genes that regulate size and/or dynamics of cell-matrix adhesions in MCF7 breast cancer cells. In a subsequent secondary screen, the 64 most effective genes were evaluated for growth factor-induced cell migration and validated by tertiary RNAi pool deconvolution experiments. Four validated hits showed significantly enlarged adhesions accompanied by reduced cell migration upon siRNA-mediated knockdown. Furthermore, loss of PPP1R12B, HIPK3 or RAC2 caused cells to exert higher traction forces, as determined by traction force microscopy with elastomeric micropillar post arrays, and led to considerably reduced force turnover. Altogether, we identified genes that co-regulate cell-matrix adhesion dynamics and traction force turnover, thereby modulating overall motility behaviour.

An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes

Mesenchymal (lamellipodial) migration is heterogeneous, although whether this reflects progressive variability or discrete, 'switchable' migration modalities, remains unclear. We present an analytical toolbox, based on quantitative single-cell imaging data, to interrogate this heterogeneity. Integrating supervised behavioral classification with multivariate analyses of cell motion, membrane dynamics, cell-matrix adhesion status and F-actin organization, this toolbox here enables the detection and characterization of two quantitatively distinct mesenchymal migration modes, termed 'Continuous' and 'Discontinuous'. Quantitative mode comparisons reveal differences in cell motion, spatiotemporal coordination of membrane protrusion/retraction, and how cells within each mode reorganize with changed cell speed. These modes thus represent distinctive migratory strategies. Additional analyses illuminate the macromolecular- and cellular-scale effects of molecular targeting (fibronectin, talin, ROCK), including 'adaptive switching' between Continuous (favored at high adhesion/full contraction) and Discontinuous (low adhesion/inhibited contraction) modes. Overall, this analytical toolbox now facilitates the exploration of both spontaneous and adaptive heterogeneity in mesenchymal migration.

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.

Disentangling Membrane Dynamics and Cell Migration; Differential Influences of F-actin and Cell-Matrix Adhesions

Cell migration is heavily interconnected with plasma membrane protrusion and retraction (collectively termed “membrane dynamics”). This makes it difficult to distinguish regulatory mechanisms that differentially influence migration and membrane dynamics. Yet such distinctions may be valuable given evidence that cancer cell invasion in 3D may be better predicted by 2D membrane dynamics than by 2D cell migration, implying a degree of functional independence between these processes. Here, we applied multi-scale single cell imaging and a systematic statistical approach to disentangle regulatory associations underlying either migration or membrane dynamics. This revealed preferential correlations between membrane dynamics and F-actin features, contrasting with an enrichment of links between cell migration and adhesion complex properties. These correlative linkages were often non-linear and therefore context-dependent, strengthening or weakening with spontaneous heterogeneity in cell behavior. More broadly, we observed that slow moving cells tend to increase in area, while fast moving cells tend to shrink, and that the size of dynamic membrane domains is independent of cell area. Overall, we define macromolecular features preferentially associated with either cell migration or membrane dynamics, enabling more specific interrogation and targeting of these processes in future.

A plastic relationship between vinculin-mediated tension and adhesion complex area defines adhesion size and lifetime

Cell-matrix adhesions are central mediators of mechanotransduction, yet the interplay between force and adhesion regulation remains unclear. Here we use live cell imaging to map time-dependent cross-correlations between vinculin-mediated tension and adhesion complex area, revealing a plastic, context-dependent relationship. Interestingly, while an expected positive cross-correlation dominated in mid-sized adhesions, small and large adhesions display negative cross-correlation. Furthermore, although large changes in adhesion complex area follow vinculin-mediated tension alterations, small increases in area precede vinculin-mediated tension dynamics. Modelling based on this mapping of the vinculin-mediated tension-adhesion complex area relationship confirms its biological validity, and indicates that this relationship explains adhesion size and lifetime limits, keeping adhesions focal and transient. We also identify a subpopulation of steady-state adhesions whose size and vinculin-mediated tension become stabilized, and whose disassembly may be selectively microtubule-mediated. In conclusion, we define a plastic relationship between vinculin-mediated tension and adhesion complex area that controls fundamental cell-matrix adhesion properties.

Cell-matrix adhesions may increase or decrease in size in response to tension; however, the factors determining which of these responses predominates remain unclear. Hernández-Varas et al. quantify the plastic relationship between adhesion size and tension and use modelling to explain this behaviour.

Non-monotonic cellular responses to heterogeneity in talin protein expression-level

Talin is a key cell-matrix adhesion component with a central role in regulating adhesion complex maturation, and thereby various cellular properties including adhesion and migration. However, knockdown studies have produced inconsistent findings regarding the functional influence of talin in these processes. Such discrepancies may reflect non-monotonic responses to talin expression-level variation that are not detectable via canonical "binary" comparisons of aggregated control versus knockdown cell populations. Here, we deployed an "analogue" approach to map talin influence across a continuous expression-level spectrum, which we extended with sub-maximal RNAi-mediated talin depletion. Applying correlative imaging to link live cell and fixed immunofluorescence data on a single cell basis, we related per cell talin levels to per cell measures quantitatively defining an array of cellular properties. This revealed both linear and non-linear correspondences between talin expression and cellular properties, including non-monotonic influences over cell shape, adhesion complex-F-actin association and adhesion localization. Furthermore, we demonstrate talin level-dependent changes in networks of correlations among adhesion/migration properties, particularly in relation to cell migration speed. Importantly, these correlation networks were strongly affected by talin expression heterogeneity within the natural range, implying that this endogenous variation has a broad, quantitatively detectable influence. Overall, we present an accessible analogue method that reveals complex dependencies on talin expression-level, thereby establishing a framework for considering non-linear and non-monotonic effects of protein expression-level heterogeneity in cellular systems.