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  1. Author Response

    Reviewer #1 (Public Review):

    In this work, Bentley et al. describe the development and use of a novel microfluidic platform to study motility of green algae. By confining algae to circular corrals of various diameters (and with a height that renders the system quasi-two-dimensional), the authors gather extremely long time series of the swimming trajectories under various degrees of lateral confinement, in the presence of several different kinds of perturbations.

    The data is presented in a number of ways, most importantly by means of transitions between the three characteristic states of motion for these algae. This allows contact to be made with ideas from nonequilibrium dynamical systems by examining the transition probabilities between those states and identifying nonequilibrium characteristics of the fluxes between them.

    Overall the work is extremely impressive in terms of the data acquisition and careful time series analysis. The work falls short though in not following through on the many interesting observations that can be deduced from the data to come to precise conclusions about the biology and physics. For example, we see in Figs. 2 and 3 the effects of confinement on the trajectories, leading to clearly chiral motion at the strongest confinement. I would have expected the next step of the analysis to be a study of this problem in the context of, say, a Fokker-Planck equation for the probability distribution function for orientations, complete with boundary conditions that encode the scattering laws that we know from prior work by Kantsler et al. and others. Similar comments can be made about the other observations, which are followed up with any clear mechanistic analysis or comparison with theory.

    The example above suggests that this paper, in its current form, is more akin to a "Methodology" paper than one that discovers new phenomena and explains them.

    We thank the reviewer for their summary of our work and for these pertinent comments. As discussed above, we performed new experiments and modelling to successfully answer the main question of why chiral circling appears in the smallest traps (highest confinement), and also why the chirality depends on light. As demonstrated in the prior Cammann et al PNAS (and to some extent also the Ostapenko et al, PRL) study, encoding the scattering laws measured from Kantsler et al for a basic swimmer produces chiral motion (circular movement). An analytical treatment in terms of FP equations already appears in these prior studies. However, the novelty here is why this circular movement should remain chiral in the time-averaged sense.

    In the revisions, we restrict ourselves to a conceptual-level where we show how a small internal asymmetry at the cellular level suffices to produce macroscopic chirality and how this depends on the size of the trap. Our new explanation reaches a precise conclusion about how a fundamentally biological phenomenon (slightly asymmetric flagellar beating in a phototactic swimmer), leads to a confinementinduced physical phenomenon (a preferred sense of circular swimming).

    In a separate follow-up study, we will extend our model to incorporate more realistic parameters from the current dataset (e.g. time-dependent speed, stochastic reorientations, shock-responses, softness of the potential etc) to understand more subtle aspects of the high-resolution data we acquired.

    Reviewer #2 (Public Review):

    The authors use microfluidic devices to follow single swimmers for long periods, measuring their movement in detail and allowing detailed statistics at a level that has never been possible before and machine learning.

    Its strength is the extraordinary detail and the doors opened by the quality of the resultant data. As such it makes a substantial contribution to a narrow field and adds slightly more subtly to an important field of full mathematically accessible descriptions of migration phenotypes.

    Its weakness is that these tools are not yet used for any particularly enlightening tests. The directed probability fluxes are interesting, but not surprising. The strength of this paper is in the method, the analysis, and the ability to generate rigorous datasets.

    We thank the reviewer for highlighting the quality and detail of our datasets, and we agree with the criticisms raised. We hope these weaknesses are now rectified in the revision. There is clearly scope to do much more now that we have access to this data, and we demonstrate this in the revision with our new model/interpretation.

    We highlight three innovations of our work that may not have been made clear in the previous draft.

    1. We have suggested a new paradigm for analysis of microbial motility and behaviour. The extraction of state transition probabilities from single-cell trajectories reveals exactly how motility changes at the subcellular level, which is much more informative than whether an organism speeds up or slows down on average. This tells us how does a given individual modulates the balance of possible behavioural states in response to their environment and also over time. These concepts apply not just microbes but to any behaving organism.

    2. The emphasis on keeping track of the ‘arrow of time’ in the analysis of movement trajectories in important, can again be applied to any organism. As discussed above, while circling behaviour or symmetry breaking in confinement may not be surprising itself (though that does not prevent the flurry of experimental and theoretical papers on this topic), we argue that the emergence of chirality in the timeaveraged trajectory is surprising and does requires more subtle treatment. We now suggest this is down to a very small amount of internal symmetry breaking – it is interesting that such a small amount of symmetry-breaking at the sub-cellular scale can manifest as robust symmetry-breaking at the macroscopic scale.

    This kind of insight has broad implications for understanding how (even simple) organisms can dramatically alter how they interact with their physical environment by effecting even minute internal adjustments. This could also motivate the design of novel biomimetic artificial devices or microswimmers.

    1. Our approach of fusing droplets to investigate rapid motility responses to chemicals has plenty of potential for drug screening and also for investigating cellular transduction pathways (e.g. functional assays of mutants). We demonstrate its operation here as proof of concept on one species for one chemical only, but there are clear advantages over traditional approaches involving setting up chemical gradients or similar, where it is impossible to get a handle on instantaneous cell reactions nor individual-level responses.
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  2. eLife assessment

    This paper reports on the development of an impressive microfluidic platform for the study of motility, and motility transitions, exhibited by single algal cells in circular confinement. Building on previous work that showed a three-state motility repertoire for certain green algae, the present work uses extremely long time series and a variety of physical perturbations to show how those dynamics can be altered by environmental conditions. The work will be of interest to a wide range of scientists studying motility and nonequilibrium dynamics, but its impact would be improved by a more insightful analysis of the voluminous data, with connections to physical principles.

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  3. Reviewer #1 (Public Review):

    In this work, Bentley et al. describe the development and use of a novel microfluidic platform to study motility of green algae. By confining algae to circular corrals of various diameters (and with a height that renders the system quasi-two-dimensional), the authors gather extremely long time series of the swimming trajectories under various degrees of lateral confinement, in the presence of several different kinds of perturbations.

    The data is presented in a number of ways, most importantly by means of transitions between the three characteristic states of motion for these algae. This allows contact to be made with ideas from nonequilibrium dynamical systems by examining the transition probabilities between those states and identifying nonequilibrium characteristics of the fluxes between them.

    Overall the work is extremely impressive in terms of the data acquisition and careful time series analysis. The work falls short though in not following through on the many interesting observations that can be deduced from the data to come to precise conclusions about the biology and physics. For example, we see in Figs. 2 and 3 the effects of confinement on the trajectories, leading to clearly chiral motion at the strongest confinement. I would have expected the next step of the analysis to be a study of this problem in the context of, say, a Fokker-Planck equation for the probability distribution function for orientations, complete with boundary conditions that encode the scattering laws that we know from prior work by Kantsler et al. and others. Similar comments can be made about the other observations, which are followed up with any clear mechanistic analysis or comparison with theory.

    The example above suggests that this paper, in its current form, is more akin to a "Methodology" paper than one that discovers new phenomena and explains them.

    Was this evaluation helpful?
  4. Reviewer #2 (Public Review):

    The authors use microfluidic devices to follow single swimmers for long periods, measuring their movement in detail and allowing detailed statistics at a level that has never been possible before and machine learning.

    Its strength is the extraordinary detail and the doors opened by the quality of the resultant data. As such it makes a substantial contribution to a narrow field and adds slightly more subtly to an important field of full mathematically accessible descriptions of migration phenotypes.

    Its weakness is that these tools are not yet used for any particularly enlightening tests. The directed probability fluxes are interesting, but not surprising. The strength of this paper is in the method, the analysis, and the ability to generate rigorous datasets.

    Was this evaluation helpful?