Collective Chemotaxis through Noisy Multicellular Gradient Sensing

J. Phys. D: Appl. Phys.

Rappel

2017

186

147

In this article, we review physics-based models of collective cell motility. We discuss a range of techniques at different scales, ranging from models that represent cells as simple self-propelled particles to phase field models that can represent a cell’s shape and dynamics in great detail. We also extensively review the ways in which cells within a tissue choose their direction, the statistics of cell motion, and some simple examples of how cell-cell signaling can interact with collective cell motility. This review also covers in more detail selected recent works on collective cell motion of small numbers of cells on micropatterns, in wound healing, and the chemotaxis of clusters of cells.

Section: Specific Examplessupporting

confidence: 58%

Section: What Is a Collective Cell Motility Model? Basic Elements mentioning

confidence: 99%

Section: What Is a Collective Cell Motility Model? Basic Elements mentioning

confidence: 99%

See 1 more Smart Citation

Physical models of collective cell motility: from cell to tissue

J. Phys. D: Appl. Phys.

Rappel

2017

186

147

“…Adaptation is a common feature of single-cell gradient sensing, and is often described with a LEGI (local excitation, global inhibition) framework. Integrating LEGI into tug-of-war models of cluster migration allows clusters to adapt and sense gradients more robustly [67, 81]. …”

Section: Models To Consider: What Is Consistent With Experiment?mentioning

confidence: 99%

Collective gradient sensing and chemotaxis: modeling and recent developments

J. Phys.: Condens. Matter

2018

Cells measure a vast variety of signals, from their environment’s stiffness to chemical concentrations and gradients; physical principles strongly limit how accurately they can do this. However, when many cells work together, they can cooperate to exceed the accuracy of any single cell. In this Topical Review, I will discuss the experimental evidence showing that cells collectively sense gradients of many signal types, and the models and physical principles involved. I also propose new routes by which experiments and theory can expand our understanding of these problems.

“…These cell-to-cell variations (CCV) can be persistent over timescales much larger than the typical motility timescale of the cell [25]. CCV has not been addressed in models of collective chemotaxis and it is not clear whether collective gradient sensing is limited by CCV or by stochastic receptor-ligand binding [7,8,[26][27][28][29][30].Using a combination of analytics and simulations, we show that unless CCV is tightly controlled, collective guidance of a cluster of cells is limited by these variations: gradient sensing is biased toward cells with intrinsically strong responses. This bias swamps the effects of stochastic ligand-receptor binding.…”

mentioning

confidence: 99%

Cell-to-cell variation sets a tissue-rheology–dependent bound on collective gradient sensing

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

Rappel

2017

When a single cell senses a chemical gradient and chemotaxes, stochastic receptor-ligand binding can be a fundamental limit to the cell's accuracy. For clusters of cells responding to gradients, however, there is a critical difference: even genetically identical cells have differing responses to chemical signals. With theory and simulation, we show collective chemotaxis is limited by cell-to-cell variation in signaling. We find that when different cells cooperate the resulting bias can be much larger than the effects of ligand-receptor binding. Specifically, when a strongly-responding cell is at one end of a cell cluster, cluster motion is biased toward that cell. These errors are mitigated if clusters average measurements over times long enough for cells to rearrange. In consequence, fluid clusters are better able to sense gradients: we derive a link between cluster accuracy, cellto-cell variation, and the cluster rheology. Because of this connection, increasing the noisiness of individual cell motion can actually increase the collective accuracy of a cluster by improving fluidity.Many cells follow signal gradients to survive or perform their functions, including white blood cells finding a wound, cells crossing a developing embryo, and cancerous cells migrating from tumors. Chemotaxis, sensing and responding to chemical gradients, is crucial in all of these examples [1,2]. Chemotaxis is traditionally studied by exposing single cells to gradients -but cells often travel in groups, not singly [3,4]. Collective cell migration is essential to development and metastasis [5], and can have remarkable effects on chemotaxis. Even when single cells cannot sense a gradient, a cluster of cells may cooperate to sense it. While collective chemotaxis is our primary focus, this "emergent" gradient sensing is found in response to many signals, including soluble chemical gradients (chemotaxis) [6][7][8], conditioned substrates (haptotaxis) [9], substrate stiffness gradients (durotaxis) [10] and electrical potential (galvanotaxis) [11,12].Cells can cooperate to sense gradients -but the physical principles limiting a cluster's sensing accuracy are not settled. For single cells, the fundamental bounds on sensing chemical concentrations and gradients are well-studied [13][14][15][16][17][18][19][20][21][22], showing unavoidable stochasticity in receptor-ligand binding limits chemotactic accuracy. Is this true for cell clusters? Is a cell cluster simply equivalent to a larger cell? No! There is an essential difference between many clustered cells and a single large cell: even clonal populations of cells can have highly variable responses to signals, due to many factors, including intrinsic variations in regulatory protein concentrations [23][24][25]. These cell-to-cell variations (CCV) can be persistent over timescales much larger than the typical motility timescale of the cell [25]. CCV has not been addressed in models of collective chemotaxis and it is not clear whether collective gradient sensing is limited by CCV or by stochasti...