UCSF

The cytoplasm is a dynamic fluid where cellular building blocks and components are continuously mixing. Cytoplasmic mixing is important for transporting intracellular material ranging from nucleotides and proteins to full organelles. In large cell types, such as ameboid and neutrophil cells, cytoplasmic streaming is coupled to membrane deformations that accompany in motility. How well or how long it takes for material to mix in cytoplasmic streams during motility remains an open question. In relatively small volumes of viscous fluids at very low Reynolds numbers, such as the cytoplasm, it should be difficult to obtain significant mixing for structures in the size range of organelles simply from streaming laminar flows. Although small things like proteins can eventually mix through diffusion, microscale structures are expected to mix on a significantly slower time frame or not at all. In this work we discovered how the giant amoeba Chaos carolinensis overcomes these limitations using a novel cytoplasmic gel state capture and release strategy to facilitate the extremely efficient mixing of its cytoplasm. While it was previously thought that the amoeba solution to gel state transitions only occurs at the trailing and leading edge of the cell body, our work indicates that these transitions occur frequently throughout the mid-cell region, driving the cytoplasmic mixing of beads and organelles. These results indicate that amoeba reaches a stable mixed state during motility in as little as one cytoplasmic stream/flow cycle, effectively making it a Bernoulli system and thus one of the fastest possible known intracellular mixers. To study cytoplasmic streams, we microinjected fluorescent beads into amoeba and recorded their movement using time-lapse microscopy for up to 12 hours. In addition to bead trajectories, we microinject histone H1 labeled with alexa 488 and tracked nuclei movement. To separate the movements of beads and nuclei from overall cell motility, we employed automated image processing to stabilize the videos, making the amoeba appear to crawl in place. Power law fits of interparticle distance over time allowed us to classify bead pair separation events into sub-diffusive, diffusive, super-diffusive, and ballistic transport regimes. Using mean squared displacement analysis, we also distinguished gel-state from liquid-state flows. This classification enabled us to calculate diffusion coefficients, mean velocities, and dwell times for each state, which we used to simulate the time required for two particle populations to achieve stable mixing. To validate these simulations, we developed a method to computationally label and track two distinct bead populations from a single-color bead injection. This approach allowed us to observe new mixing events frame by frame and showed strong agreement between simulated and experimental results. Here we show the innovative strategy through which the giant amoeba Chaos carolinensis achieves efficient cytoplasmic mixing despite its reliance on low-Reynolds-number, laminar flow. Our results highlight the role of mid-cell sol-to-gel transitions in facilitating efficient mixing, enabling material exchange between cytoplasmic layers and allowing complete intracellular mixing within a single flow cycle. Novel computational approaches validated the robustness of these dynamics through simulation, showing alignment with experimental data for both bead and nuclear trajectories. Moreover, pseudopod modulation was found to have minimal impact on mixing efficiency, underscoring the unique contribution of sol-to-gel state dynamics. This work establishes a robust framework for studying intracellular mixing, with potential applications across cellular systems characterized by cytoplasmic streaming.