Understanding the locomotion of microorganisms is essential for insights into microbial ecology, infection, and colonization processes. Although two-dimensional microscopy has been widely used to study microswimmer motility, it does not capture the full extent of their three-dimensional (3D) movement. Recent advances in defocused particle tracking, holographic tracking velocimetry, and stereo-microscopy face challenges in achieving high resolution at larger particle densities and tracking multiple microswimmers in suspension. In this work, we introduce a novel multi-camera microscopy system that significantly improves the accuracy of 3D microswimmer tracking. Our system uses four sCMOS cameras to image microorganisms within a 2.5 × 2.5 × 2 mm3. We assess the performance of our microscopy system by tracking a population of the unicellular motile algae Chlamydomonas reinhardtii. An in-house tracking algorithm based on the projective geometry framework enables tracking with reprojection errors below 0.3 body lengths. This system supports imaging and tracking particle source densities of 0.32, higher than other existing single camera 3D microscopy techniques. Analysis of C. reinhardtii trajectories in 3D reveals a predominance of left-handed chirality and helical swimming patterns. Moreover, our 3D tracking data provide translational and rotational diffusion coefficients that differ from those obtained using traditional two-dimensional methods.

The accumulation of motile cells at solid interfaces increases the rate of surface encounters and the likelihood of surface attachment, leading to surface colonization and biofilm formation. The cell density distribution in the vicinity of a physical boundary is influenced by the interactions between the microswimmers and their physical environment, including hydrodynamic and steric interactions, as well as by stochastic effects. Disentangling the contributions of these effects remains an experimental challenge. Here, we use a custom-made four-camera view microscope to track a population of motile puller-type Chlamydomonas reinhardtii in a relatively unconstrained three-dimensional (3D) domain. Our experiments yield an extensive sample of 3D trajectories including cell-surface encounters with a planar wall, from which we extract a full description of the dynamics and the stochasticity of swimming cells. We use this large data sample and combine it with Monte Carlo simulations to determine the link between the dynamics at the single-cell level and the cell density. Our experiments demonstrate that the near-wall scattering is bimodal, corresponding to steric and hydrodynamic effects. We find, however, that this near-wall dynamics has little influence on the cell accumulation at the surface. On the other hand, we present evidence of a cell-induced surface-directed rotation leading to a vertical orbiting behavior and hopping trajectories, consistent with long-range hydrodynamic effects. We identify this long-range effect to be at the origin of the significant surface accumulation of cells.

Abstract: Obtaining accurate experimental data from Lagrangian tracking and tomographic velocimetry requires an accurate camera calibration consistent over multiple views. Established calibration procedures are often challenging to implement when the length scale of the measurement volume exceeds that of a typical laboratory experiment. Here, we combine tools developed in computer vision and non-linear camera mappings used in experimental fluid mechanics, to successfully calibrate a four-camera setup that is imaging inside a large tank of dimensions ∼10×25×6m3. The calibration procedure uses a planar checkerboard that is arbitrarily positioned at unknown locations and orientations. The method can be applied to any number of cameras. The parameters of the calibration yields direct estimates of the positions and orientations of the four cameras as well as the focal lengths of the lenses. These parameters are used to assess the quality of the calibration. The calibration allows us to perform accurate and consistent linear ray-tracing, which we use to triangulate and track fish inside the large tank. An open-source implementation of the calibration in Matlab is available. Graphic abstract: [Figure not available: see fulltext.].

Mixing and spreading of different liquids are omnipresent in nature, life and technology, such as oil pollution on the sea, estuaries, food processing, cosmetic and beverage industries, lab-on-a-chip devices, and polymer processing. However, the mixing and spreading mechanisms for miscible liquids remain poorly characterized. Here, we show that a fully soluble liquid drop deposited on a liquid surface remains as a static lens without immediately spreading and mixing, and simultaneously a Marangoni-driven convective flow is generated, which are counterintuitive results when two liquids have different surface tensions. To understand the dynamics, we develop a theoretical model to predict the finite spreading time and length scales, the Marangoni-driven convection flow speed, and the finite timescale to establish the quasi-steady state for the Marangoni flow. The fundamental understanding of this solutal Marangoni flow may enable driving bulk flows and constructing an effective drug delivery and surface cleaning approach without causing surface contamination by immiscible chemical species.