Cells can sense and respond to the mechanical properties of their microenvironment through changes in gene expression and cell behavior, a phenomenon known as mechanotransduction. Interestingly, not only do cells sense mechanical cues, but they can also retain information about mechanical properties once they are no longer exposed to them. This type of long-term mechanotransduction, termed “mechanical memory,” may have important implications for development, differentiation, and disease.
When cells cultured on soft substrates are transferred to a stiff substrate (termed the “priming phase”), stiffness triggers specific phenotypic—mainly cytoskeletal and nuclear—changes. Once cells are moved back to the initial soft substrate (the dissipative phase), their ability to retain these acquired phenotypic features will depend on the length of time spent on the stiff substrate. The developed mechanical memory can range from no memory acquired to permanent memory (such as cell differentiation). While this phenomenon depends both on the microenvironment and the accumulated mechanical history of the cell itself, there is still limited understanding of the mechanism by which it is developed, maintained, and eventually lost.
In parallel, mathematical models have also attempted to explain the dynamics of mechanical memory and how it may dictate cell differentiation. However, most have failed to capture two important features that remain unclear: 1) the difference in timescale between cell response to mechanical signals and development of mechanical memory and 2) the difference in the persistence time of mechanical memory developed. Still, a better understanding of mechanical memory could help us engineer cell dynamics for experiments and therapeutics. With this in mind, Price and colleagues develop a mathematical framework in the recent publication "Dynamic self-reinforcement of gene expression determines acquisition of cellular mechanical memory" that captures these features. This article is part of the November 16, 2021 issue of Biophysical Journal.
In brief, this study puts forward a mathematical model to describe the dynamics—both slow and fast—of acquisition and persistence of mechanical memory. The model first introduces the variable x, which measures net cell mechanoactivation in response to substrate stiffness. The variable x represents all stiff-activated proteins like filamentous actin (F-actin), vinculin, and transcription factors YAP or MKL-1, among others. On the basis of experimental evidence, x is then incorporated in a positive reinforcement loop where increased stiffness of the substrate will trigger transcriptional activity of several components of x, thereby causing increased F-actin polymerization, focal adhesion density, and cell contractility, thusfurther increasing individual components of x. The resulting transcriptional reinforcement couples mechanosensation to nuclear activity. In their model, the authors vary only the length of (priming) time when cells are exposed to the stiff substrate, demonstrating that the degree of positive reinforcement (and thus memory acquisition) depends solely on the timescale of priming.
- Short priming times: cells adapt temporarily to substrate stiffness, but mechanical signaling cannot increase reinforcement (transcriptional activity) sufficiently to generate mechanical memory (no memory.)
- Intermediate priming times: mechanical signaling increases reinforcement but leads to only temporary memory. The model here predicts a continuous range of memory persistence times, from much shorter than priming time to longer than priming time, depending on the time and stiffness of the priming phase.
- Long priming times: reinforcement from mechanical signaling becomes strong enough to result in permanent memory in which the cell phenotype persists even if the substrate is switched back.
Altogether, the proposed mathematical framework identifies a nonlinear coupling between mechanical signaling and transcriptional evolution that can explain the general features of mechanical memory. Although further experimental studies are required to extend this model and include mechanistic details, this study may represent an important foundation for experimental studies and cell-based therapies that aim to engineer cell dynamics based on microenvironment mechanics.