Plasticity in the Macromolecular-Scale Causal Networks of Cell Migration

Lock, John G.; Mamaghani, Mehrdad Jafari; Shafqat-Abbasi, Hamdah; Gong, Xiaoyi; Tyrcha, Joanna; Strömblad, Staffan

doi:10.1371/journal.pone.0090593

Cited by 22 publications

(57 citation statements)

References 72 publications

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“…Combining the microscopy and cell-tracking data with simulations in which the local microenvironments are defined a priori could be used to identify microenvironment cues of cell behavior that would be analogous to the open-loop simulations described here. In cases where datasets contain a large number of independent variables, or if no clear hypotheses exist, statistical techniques such as correlation analysis, mutual information, or granger causality (45,46) could be used to generate an initial hypothesis to test in simulations. In systems that have incomplete datasets, hypothesized distributions can be integrated into the agent's behavior.…”

Section: Discussionmentioning

confidence: 99%

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

Cotter

Schüttler

Igoshin

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

145

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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 datadriven 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.agent-based simulation | image processing | emergent behavior | fluorescent imaging | cell communication C ollective cell migration is essential for many developmental processes, including fruiting body development of myxobacteria (1) and Dictyostelium (2), embryonic gastrulation (3, 4), and neural crest development (5). Conversely, cancer cell metastases represent detrimental migratory events that disseminate dysfunctional cells (6). In all these processes, a population of cells leaves its current location and migrates in a coordinated manner to new locations where motility becomes reduced. Remarkable progress has been made in studying the intracellular machinery of these organisms (7). Much less is known about the systemlevel coordination of cell migration. Cell movement in these systems is a 3D, dynamic process coordinated by a combination of diverse physical and chemical cues acting on the cells (3,5,8). Recent developments in tracking individual cell movement in vivo have provided unprecedented detail and revealed surprising levels of heterogeneity (5, 7). Reverse engineering of how these individual cell movements lead to collective migration patterns has proved difficult. Whereas computational models are able to test whether a given set of ad hoc assumptions lead to e...

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Section: Discussionmentioning

confidence: 99%

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

Cotter

Schüttler

Igoshin

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

145

View full text Add to dashboard Cite

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“…2B, Videos S1-3). Future studies will focus on characterizing morphological state properties in more depth by quantifying cytoskeletal organization in addition to whole-cell shape, incorporating signaling status from live or fixed-cell fluorescent reporters in post-capture analysis 24,54 , and combining model-annotated state-space dynamics with motility measurements to establish temporal relationships between morphology and migratory behavior 27,55,56 .…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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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 (HMM) 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.

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“…The feature extractor calculates 13 parameters of motion: (1) mean displacement, (2) displacement variance, (3) minimum and (4) maximum displacement, (5) the mean turning angle, (6) the mean turning angle magnitude, (7) turning angle variance, (8) total distance traveled, (9) net distance traveled (distance from starting position to final position), (10) progressivity of motion (net distance as a fraction of total distance) [48], (11) linearity of motion (Pearson's r 2 ), (12) monotonicity of motion (Spearman's ρ), and (13) the convex hull area of the cell motility track [17]. These heurstics are commonly employed in the quantitative cell motility literature [21], [48], [49], [17]. The RF classifier hyperparameters were optimized by grid search for each application.…”

Section: Baseline Motility Classificationmentioning

confidence: 99%

Deep convolutional and recurrent neural networks for cell motility discrimination and prediction

Kimmel

Brack

Marshall

2017

Preprint

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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.

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Plasticity in the Macromolecular-Scale Causal Networks of Cell Migration

Cited by 22 publications

References 72 publications

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

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

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

Deep convolutional and recurrent neural networks for cell motility discrimination and prediction

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