Place yourself in a bumper car at a carnival waiting to bump into your friends. Soon enough you hear the small engine of your bumper car start and you begin to move around, bumping into anyone in your way. While the motion of your car is mostly controlled by the steering wheel, random events—like fluctuations in the motor power, your car hitting small bumps on the floor, and other cars hitting you—can affect the motion as well. What if I told you that a cell and its parts function in a similar way? Just as your car is powered by electricity, molecular motors—bio-molecules that can convert chemical energy into mechanical work—power the movement of living organisms by generating forces. In order to produce these forces, molecular motors depend on an organic molecule called ATP [Footnote: Adenosine TriPhosphate]. And just like the fluctuations in the motor power of the bumper car, random fluctuations can also be produced by the molecular motors.
The motion caused by molecular motors is necessary for the functionality of the cell—for example, division and contraction. However, it’s not this directed motion that’s studied in today’s paper, but rather the random fluctuations that accompany it. But how can we extract useful data from random movements like those in the cytoplasm? One way is to measure the mean squared displacement (MSD) of a particle in the fluid. The MSD is a measure of how far a particle moves from its starting point over time. Going back to the example of the bumper car, you could find your MSD by tracing your path and seeing how far you have moved from your starting point over time1.
To investigate the motion of particles in the cytoplasm, Guo and colleagues injected tiny particles into the cells and tracked their motion using confocal microscopy—a technique that allows for the precise tracking of the 3D position of micro-particles. After tracking the particles over time, Guo calculated the MSDs of the particles2.
Guo and colleagues observed that at short timescales, t ≤ 0.1s, the MSDs were nearly time independent, meaning that they did not change over time (see Figure 2A). This type of motion is typically observed in elastic solids, where particles can never move very far from their starting points. At longer timescales measured, 10s ﹥t ≥ 0.1s, the MSDs grew linearly with time. This type of motion is called Brownian motion and is usually observed in particles moving in viscous fluids under the influence of thermal forces. This association between linear MSDs and Brownian motion is strong enough that researchers have sometimes assumed that that particles inside cells move primarily due to thermal forces. However, as discussed earlier, molecular motors generate forces inside the cytoplasm. Is it possible that random forces from molecular motors affect the motion of the particles?
In order to answer this question, Guo and his colleagues reduced the amount of ATP in cultured cells, thus reducing the activity of the molecular motors. They observed that the MSDs of particles inside ATP-depleted cell didn’t exhibit the linear MSDs seen in the untreated cells (see Figure 2B). This observation means that forces causing Brownian motion in the cytoplasm were ATP-dependent and therefore not generated by random thermal motion alone.
In short, Guo and colleagues showed that molecular motors impact the random motion inside the cytoplasm of a cell. The team proved this by measuring the MSD of particles inside cultured cells. They then depleted ATP in the cells to observe any changes in the MSDs of the particles inside. They found that movement inside the cytoplasm was largely affected by random molecular forces produced by molecular motors and not solely due to thermal forces. However, this discovery raised more questions. For example: why do these molecular motors, which exert directed forces, exhibit random movements? We’ll answer this question in a follow-up post by considering the elastic network that couples molecular motors.
1. Note that a post by Christine Middleton has gone over a slightly different application of the MSD here: https://softbites.org/2018/04/25/the-matter-of-maternal-mucus-permeability-and-preterm-birth.↩
2. Mean Squared Displacement: <Δr2(τ)> ; < Δr(τ) > = r(t+τ)-r(t) ↩
3. The purpose of normalizing the data is to more easily compare the data between different particle sizes. ↩