Guidelines and definitions for research on epithelial-mesenchymal transition.

Epithelial-mesenchymal transition (EMT) encompasses dynamic changes in cellular organization from epithelial to mesenchymal phenotypes, which leads to functional changes in cell migration and invasion. EMT occurs in a diverse range of physiological and pathological conditions and is driven by a conserved set of inducing signals, transcriptional regulators and downstream effectors. With over 5,700 publications indexed by Web of Science in 2019 alone, research on EMT is expanding rapidly. This growing interest warrants the need for a consensus among researchers when referring to and undertaking research on EMT. This Consensus Statement, mediated by 'the EMT International Association' (TEMTIA), is the outcome of a 2-year-long discussion among EMT researchers and aims to both clarify the nomenclature and provide definitions and guidelines for EMT research in future publications. We trust that these guidelines will help to reduce misunderstanding and misinterpretation of research data generated in various experimental models and to promote cross-disciplinary collaboration to identify and address key open questions in this research field. While recognizing the importance of maintaining diversity in experimental approaches and conceptual frameworks, we emphasize that lasting contributions of EMT research to increasing our understanding of developmental processes and combatting cancer and other diseases depend on the adoption of a unified terminology to describe EMT.

Mechanisms and in vivo functions of contact inhibition of locomotion.

Contact inhibition of locomotion (CIL) is a process whereby a cell ceases motility or changes its trajectory upon collision with another cell. CIL was initially characterized more than half a century ago and became a widely studied model system to understand how cells migrate and dynamically interact. Although CIL fell from interest for several decades, the scientific community has recently rediscovered this process. We are now beginning to understand the precise steps of this complex behaviour and to elucidate its regulatory components, including receptors, polarity proteins and cytoskeletal elements. Furthermore, this process is no longer just in vitro phenomenology; we now know from several different in vivo models that CIL is essential for embryogenesis and in governing behaviours such as cell dispersion, boundary formation and collective cell migration. In addition, changes in CIL responses have been associated with other physiological processes, such as cancer cell dissemination during metastasis.

The front and rear of collective cell migration.

Collective cell migration has a key role during morphogenesis and during wound healing and tissue renewal in the adult, and it is involved in cancer spreading. In addition to displaying a coordinated migratory behaviour, collectively migrating cells move more efficiently than if they migrated separately, which indicates that a cellular interplay occurs during collective cell migration. In recent years, evidence has accumulated confirming the importance of such intercellular communication and exploring the molecular mechanisms involved. These mechanisms are based both on direct physical interactions, which coordinate the cellular responses, and on the collective cell behaviour that generates an optimal environment for efficient directed migration. The recent studies have described how leader cells at the front of cell groups drive migration and have highlighted the importance of follower cells and cell-cell communication, both between followers and between follower and leader cells, to improve the efficiency of collective movement.

Collective durotaxis along a self-generated stiffness gradient in vivo.

Collective cell migration underlies morphogenesis, wound healing and cancer invasion1,2. Most directed migration in vivo has been attributed to chemotaxis, whereby cells follow a chemical gradient3-5. Cells can also follow a stiffness gradient in vitro, a process called durotaxis3,4,6-8, but evidence for durotaxis in vivo is lacking6. Here we show that in Xenopus laevis the neural crest-an embryonic cell population-self-generates a stiffness gradient in the adjacent placodal tissue, and follows this gradient by durotaxis. The gradient moves with the neural crest, which is continually pursuing a retreating region of high substrate stiffness. Mechanistically, the neural crest induces the gradient due to N-cadherin interactions with the placodes and senses the gradient through cell-matrix adhesions, resulting in polarized Rac activity and actomyosin contractility, which coordinates durotaxis. Durotaxis synergizes with chemotaxis, cooperatively polarizing actomyosin machinery of the cell group to prompt efficient directional collective cell migration in vivo. These results show that durotaxis and dynamic stiffness gradients exist in vivo, and gradients of chemical and mechanical signals cooperate to achieve efficient directional cell migration.

Mechanisms of Neural Crest Migration.

Neural crest cells are a transient embryonic cell population that migrate collectively to various locations throughout the embryo to contribute a number of cell types to several organs. After induction, the neural crest delaminates and undergoes an epithelial-to-mesenchymal transition before migrating through intricate yet characteristic paths. The neural crest exhibits a variety of migratory behaviors ranging from sheet-like mass migration in the cephalic regions to chain migration in the trunk. During their journey, neural crest cells rely on a range of signals both from their environment and within the migrating population for navigating through the embryo as a collective. Here we review these interactions and mechanisms, including chemotactic cues of neural crest cells' migration.

Actomyosin forces in cell migration: Moving beyond cell body retraction.

In textbook illustrations of migrating cells, actomyosin contractility is typically depicted as the contraction force necessary for cell body retraction. This dogma has been transformed by the molecular clutch model, which acknowledges that actomyosin traction forces also generate and transmit biomechanical signals at the leading edge, enabling cells to sense and shape their migratory path in mechanically complex environments. To fulfill these complementary functions, the actomyosin system assembles a gradient of contractile energy along the front-rear axis of migratory cells. Here, we highlight the hierarchic assembly and self-regulatory network structure of the actomyosin system and explain how the kinetics of different nonmuscle myosin II (NM II) paralogs synergize during contractile force generation. Our aim is to emphasize how protrusion formation, cell adhesion, contraction, and retraction are spatiotemporally integrated during different modes of migration, including chemotaxis and durotaxis. Finally, we hypothesize how different NM II paralogs might tune aspects of migration in vivo, highlighting future research directions.

Self-organized collective cell behaviors as design principles for synthetic developmental biology.

Over the past two decades, molecular cell biology has graduated from a mostly analytic science to one with substantial synthetic capability. This success is built on a deep understanding of the structure and function of biomolecules and molecular mechanisms. For synthetic biology to achieve similar success at the scale of tissues and organs, an equally deep understanding of the principles of development is required. Here, we review some of the central concepts and recent progress in tissue patterning, morphogenesis and collective cell migration and discuss their value for synthetic developmental biology, emphasizing in particular the power of (guided) self-organization and the role of theoretical advances in making developmental insights applicable in synthesis.

Generative models of morphogenesis in developmental biology.

Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods.

Mechanically guided cell fate determination in early development.

Cell fate determination, a vital process in early development and adulthood, has been the focal point of intensive investigation over the past decades. Its importance lies in its critical role in shaping various and diverse cell types during embryonic development and beyond. Exploration of cell fate determination started with molecular and genetic investigations unveiling central signaling pathways and molecular regulatory networks. The molecular studies into cell fate determination yielded an overwhelming amount of information invoking the notion of the complexity of cell fate determination. However, recent advances in the framework of biomechanics have introduced a paradigm shift in our understanding of this intricate process. The physical forces and biochemical interplay, known as mechanotransduction, have been identified as a pivotal drive influencing cell fate decisions. Certainly, the integration of biomechanics into the process of cell fate pushed our understanding of the developmental process and potentially holds promise for therapeutic applications. This integration was achieved by identifying physical forces like hydrostatic pressure, fluid dynamics, tissue stiffness, and topography, among others, and examining their interplay with biochemical signals. This review focuses on recent advances investigating the relationship between physical cues and biochemical signals that control cell fate determination during early embryonic development.