Periodic Reporting for period 1 - CellMechSensE (Cell mechanosensing in the extracellular matrix)
Periodo di rendicontazione: 2020-06-01 al 2022-05-31
In our body, cells detect and respond to a variety of guiding cues from their extracellular environment. These signals ensure constant regulation of cellular behavior. However, if cells mis-interpret the signals and fail to perform their functional tasks, then disastrous effects, such as cancer metastasis or diabetes can take place.
Recently, experiments using artificial cellular environments have established that key cell behaviors can strongly depend on the mechanics of their substrate. Thus, it is thought that cells perform mechanosensation: they probe their surroundings and adapt to the mechanical response they measure. Yet, a clear understanding of how cells perform mechanosensation in their natural extracellular matrix environment is still lacking. Indeed, the large inherent structural disorder of this network strongly limits the mechanical information that can be inferred from local cell-scale measurements. Moreover, the matrix also features an intricate nonlinear mechanical response that is largely unexplored at the cellular scale. This combination poses a puzzle for what information cells can glean by mechanically probing their ECM environment: How does the interplay between inherent nonlinearities and structural disorder physically limit cellular mechanosensation?
This project aims at establishing a probing mechanism cells can employ to accurately infer the mechanical properties of their natural surroundings. We proposed a novel mechanism termed nonlinear mechanosensation, in which cells can actually take advantage of local nonlinearities to accurately sense their mechanical environment. This model relates local nonlinearities to macroscopic linear properties and brings new central theoretical knowledge to understand how cells behave individually and collectively by mechanically interacting with their surrounding environment.
Recently, experiments using artificial cellular environments have established that key cell behaviors can strongly depend on the mechanics of their substrate. Thus, it is thought that cells perform mechanosensation: they probe their surroundings and adapt to the mechanical response they measure. Yet, a clear understanding of how cells perform mechanosensation in their natural extracellular matrix environment is still lacking. Indeed, the large inherent structural disorder of this network strongly limits the mechanical information that can be inferred from local cell-scale measurements. Moreover, the matrix also features an intricate nonlinear mechanical response that is largely unexplored at the cellular scale. This combination poses a puzzle for what information cells can glean by mechanically probing their ECM environment: How does the interplay between inherent nonlinearities and structural disorder physically limit cellular mechanosensation?
This project aims at establishing a probing mechanism cells can employ to accurately infer the mechanical properties of their natural surroundings. We proposed a novel mechanism termed nonlinear mechanosensation, in which cells can actually take advantage of local nonlinearities to accurately sense their mechanical environment. This model relates local nonlinearities to macroscopic linear properties and brings new central theoretical knowledge to understand how cells behave individually and collectively by mechanically interacting with their surrounding environment.
To determine a relevant probing mechanism for mechanosensation, we simulated the mechanical interactions between a cell and its surrounding environment. We investigated mechanosensation from the perspective of a cell as an ideal mechanosensor inside a disordered matrix. For that purpose, we built a computational model describing both the matrix, via modelling the mechanics of a disordered network of nonlinear fibers, and the cell, modelled as a force applied locally on the network. We measured the stiffness of the network at this cell scale by probing the network at various locations and compared these measurements to the macroscopic modulus of the same network.
The local stiffness measurements revealed a striking behavior. At low forces, the stiffness measured is independent of the applied force and vary strongly from one location to another in the network: cells can only acquire unreliable information about the mechanical landscape of their environment. Remarkably, as the force is increased the local stiffness that cells could measure inside a fibrous matrix becomes reliable: the probing force sets the measured stiffness, this measured stiffness becomes increasingly robust to local network fluctuations and strongly correlated to the macroscopic modulus of the networks. We confirmed these behaviors via experimental measurements performed using optical tweezers in reconstituted biopolymers. Therefore, at low force a single mechanical meaurement is a poor estimator of the network’s average mechanical properties: mechanosensing is strongly limited by structural heterogeneities. By contrast, the local mechanical response of fiber networks becomes largely insensitive to structural disorder at large force and captures the global network properties.
In this large force regime, we identified the emergence of a growing region over which nonlinearities develop. In this section of the network, the distribution of forces is drastically different: a subnetwork of ropes carries most of the load and these tensed fibers are surrounded by compressed buckled bonds. By looking at the density of buckled bonds, we determined the nonlinear region length-scale and showed that this length increases with the imposed force. Because of the emergence of a tensed subnetwork in this region, this section of the network is much stiffer than its surroundings. We confirmed experimentally the emergence of this stiffened nonlinear region by measuring the stiffness at various locations in the wake of contractile cells. Using the observed growth of the stiffened region, we built a scaling argument that adequately explains the measured stiffness fluctuations at large forces.
The local stiffness measurements revealed a striking behavior. At low forces, the stiffness measured is independent of the applied force and vary strongly from one location to another in the network: cells can only acquire unreliable information about the mechanical landscape of their environment. Remarkably, as the force is increased the local stiffness that cells could measure inside a fibrous matrix becomes reliable: the probing force sets the measured stiffness, this measured stiffness becomes increasingly robust to local network fluctuations and strongly correlated to the macroscopic modulus of the networks. We confirmed these behaviors via experimental measurements performed using optical tweezers in reconstituted biopolymers. Therefore, at low force a single mechanical meaurement is a poor estimator of the network’s average mechanical properties: mechanosensing is strongly limited by structural heterogeneities. By contrast, the local mechanical response of fiber networks becomes largely insensitive to structural disorder at large force and captures the global network properties.
In this large force regime, we identified the emergence of a growing region over which nonlinearities develop. In this section of the network, the distribution of forces is drastically different: a subnetwork of ropes carries most of the load and these tensed fibers are surrounded by compressed buckled bonds. By looking at the density of buckled bonds, we determined the nonlinear region length-scale and showed that this length increases with the imposed force. Because of the emergence of a tensed subnetwork in this region, this section of the network is much stiffer than its surroundings. We confirmed experimentally the emergence of this stiffened nonlinear region by measuring the stiffness at various locations in the wake of contractile cells. Using the observed growth of the stiffened region, we built a scaling argument that adequately explains the measured stiffness fluctuations at large forces.
This project allowed identifying a novel mechanism, termed nonlinear mechanosensation, with which cells can actually take advantage of local nonlinearities to accurately sense their mechanical environment. This mechanosensory strategy is based on the emergence and growth with force of a characteristic length scale over which nonlinearities develop. As the locally applied probing force triggers this nonlinear response over extended length-scales, the local probe then behaves as an effectively larger probing device: the measurement now averages the contribution of many heterogeneities and captures the linear macroscopic modulus of its surrounding network.
We also identified that the nonlinearities play fundamentally different roles at the micro- and macroscopic scales. Indeed, the dependences of the macroscopic modulus with loading and fiber density are set by the constitutive mechanical response of the fibers. On the contrary, at the local scale, the response is independent of the fibers micromechanics. Actually, the local measurement is determined by the growth of the effective probe and the linear response of its surrounding network.
Therefore, this project brings a major conceptual advance that changes the way we view mechanical interactions between cells and the matrix. Our results will enable improving the accuracy of recently introduced inference methods and attract both the biology, physics and engineering communities.
We also identified that the nonlinearities play fundamentally different roles at the micro- and macroscopic scales. Indeed, the dependences of the macroscopic modulus with loading and fiber density are set by the constitutive mechanical response of the fibers. On the contrary, at the local scale, the response is independent of the fibers micromechanics. Actually, the local measurement is determined by the growth of the effective probe and the linear response of its surrounding network.
Therefore, this project brings a major conceptual advance that changes the way we view mechanical interactions between cells and the matrix. Our results will enable improving the accuracy of recently introduced inference methods and attract both the biology, physics and engineering communities.