Inferring intraciliary dynamics from the gliding motility of Chlamydomonas reinhardtii

The unicellular microalga Chlamydomonas reinhardtii is widely recognized as a premier model living microswimmer for physicists and biophysicists. However, the interest around C. reinhardtii goes beyond its swimming capabilities. In fact, light can drastically alter its behavior: under blue illumination, the cell attaches to a nearby surface and intermittently glides on it. Such a gliding motility is powered by molecular-motor proteins operating on the cell’s cilia, and the related machinery has established the cell as a prime reference for the study of intraciliary-transport mechanisms. This is what we focus on in the present work, by combining in-line holographic microscopy -which leads to unprecedented spatial and temporal resolutions on the gliding dynamics -and statistical inference. We show that, while gliding, the cells exhibit anomalous-diffusive features, including Lorentzian-like distributions of displacements, which are reminiscent of enhanced search strategies. The latter may be exploited by the cells to facilitate colony formation, or, more broadly, by organisms possessing an intraciliary-transport machinery for the transport of cargo molecules and signaling. Furthermore, gliding trajectories, by being intermittent, are valid candidates to infer forces at the molecular-motor scale that are necessary for the cells to move, or symetrically, to transport cargo molecules. We report a gliding threshold of about 20 pN, compatible with the activity of single molecular motors.

💡 Research Summary

This paper investigates the gliding motility of the unicellular alga Chlamydomonas reinhardtii that occurs when the cells are exposed to blue light and adhere to a surface. The authors combine in‑line holographic microscopy with statistical inference to obtain three‑dimensional trajectories of individual cells with nanometer spatial and millisecond temporal resolution. By fitting the recorded holograms with Mie theory, they extract the cell’s effective radius (≈4 µm), refractive index (≈1.40) and the axial position with an uncertainty of about 0.5 µm. The gliding motion is shown to be intermittent: cells switch between active forward displacements (≈0.9 µm s⁻¹) and pause phases during which the number of intraflagellar‑transport (IFT) trains on the two cilia is balanced.

Analysis of the in‑plane mean‑squared displacement (MSD) reveals a super‑diffusive scaling ⟨Δr²⟩∝τ^β with β≈1.3–1.5 at short lag times, deviating from the linear τ dependence of pure Brownian motion. Corresponding displacement probability density functions display Lorentzian‑like heavy tails that are τ‑independent, contrasting with the Gaussian distribution observed for cells that remain paused for long periods (where the MSD is linear and the diffusion coefficient D_pause≈8.8×10⁻³ µm² s⁻¹).

To reproduce these statistics, the authors simulate trajectories that alternate between a standard Brownian process and Lévy flights with exponent α=1 (extreme flights). The simulated MSDs and PDFs match the experimental data without additional fitting parameters, indicating that the intermittent combination of passive diffusion and occasional long jumps captures the essential physics of gliding.

From the measured velocities and the known geometry of the IFT‑driven pulling mechanism, the authors estimate a minimal propulsive force of ≈20 pN, which is compatible with the stall force of a single dynein‑1b motor. This suggests that only a few motors are sufficient to generate the observed motion.

The paper also models the two preferred in‑plane positions as minima of a double‑well potential, extracting an effective spring constant of 0.5–2 nN µm⁻¹ that quantifies the energetic barrier between the states.

Overall, the study demonstrates that high‑resolution holographic tracking combined with anomalous‑diffusion analysis provides a non‑invasive window onto the molecular‑scale forces generated by IFT machinery. The observed super‑diffusive, Lévy‑flight‑like dynamics imply that gliding cells explore their environment far more efficiently than a purely diffusive search would allow, potentially facilitating colony formation and efficient nutrient or signal acquisition. The methodology and insights are broadly applicable to other microorganisms and synthetic microswimmers that rely on internal motor proteins for locomotion.