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

Abstract

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...

Data-driven models reveal mutant cell behaviors important for myxobacterial aggregation

“…The results ( Figure 2A) indicate that aggregation of WT mixed with pilC cells is 98 slightly slower than WT aggregation (dataset from [7]. On the other hand, WT cells 99 mixed with csgA show faster aggregation.…”

“…Here 9 we develop a new approach that leverages data-driven modeling to determine whether a 10 statistically significant trend in cell behavior results in biologically significant alteration 11 of the multicellular program. We demonstrate this approach by focusing on full or aggregation [7,34]. These behaviors include reduced movement inside the aggregate and 24 a bias in directed movement toward the aggregation centers, likely via chemotaxis [34].…”

“…Upon amino acid limitation, M. xanthus cells move into 19 three-dimensional aggregates called fruiting bodies where they sporulate [14,18,23]. 20 Recent studies based on cell tracking have provided unprecedented detail of cell 21 movement during development [7]. In combination with mathematical modeling, these 22 datasets unambiguously identified individual cell behaviors that are essential for 23 42 help of A-motility system that uses a novel molecular motor and focal adhesion 43 complexes [9,21].…”

“…One of the 3 most remarkable features of all types of development is how robust it is in the face of 4 genetic and environmental perturbations, suggesting that backup systems are in 5 place [27]. While molecular genetics has identified mutations that impede multicellular 6 development, even single mutations create downstream effects that influence multiple 7 aspects of cell behavior and physiology. It is frequently difficult to ascertain which of 8 the behavioral changes are deleterious to development and which can be tolerated.…”

“…Although much is known about M. xanthus 53 aggregation [19,28,29,33], few quantitative data sets describe mutant cell movement 54 during aggregation and the mechanism of their rescue. 55 To identify motility behaviors affecting mutant cell aggregation, we extended our 56 previously developed approach that combines individual cell tracking with simulations 57 driven by the accumulated cell behavior data [7]. Directly applying experimental cell 58 data to simulations allowed us to fully investigate the effect of each change in the 59 mutant motility behavior on their aggregation.…”