Cilia are small hairlike organelles with a microtubule-based core structure that protrude from the cell surface. They are found on most eukaryotic cells (Nachury and Mick, 2019) and can be broadly classified into two categories: primary and motile. Primary cilia, of which there is only one on each cell, have primarily sensory functions (as receptors for chemical, mechanical, or other signals) (Zimmermann, 1898; Berbari et al., 2009; Hilgendorf et al., 2016; Spasic and Jacobs, 2017; Ferreira et al., 2019). Due to their shape and their role in signalling, they are often referred to as ‘the cell’s antenna’ (Marshall and Nonaka, 2006; Malicki and Johnson, 2017). Motile cilia, typically appearing in larger numbers (Brooks and Wallingford, 2014; Spassky and Meunier, 2017), move the surrounding fluid by beating in an asymmetric fashion (Golestanian et al., 2011; Gilpin et al., 2020), and often with some degree of coordination (Uchida and Golestanian, 2010; Elgeti and Gompper, 2013). They play a key role in a number of processes, including the swimming and feeding of microorganisms (Guasto et al., 2012; Lisicki et al., 2019), mucus clearance in airways (Bustamante-Marin and Ostrowski, 2017), fluid transport in brain ventricles (Faubel et al., 2016), and egg transport in Fallopian tubes. However, there are exceptions to this classification. Primary cilia in the vertebrate left-right organiser are motile and drive a lateral fluid flow that triggers, through a mechanism that is not yet fully understood, a distinct signalling cascade determining the body laterality (Essner et al., 2002). There is also mounting evidence that motile cilia can have various sensory roles (Bloodgood, 2010), including chemical reception (Shah et al., 2009). Adversely, receptors localised on motile cilia, such as ACE2, can also act as entry points for viruses including SARS-CoV-2 (Lee et al., 2020). Some chemosensory systems, including vomeronasal (Leinders-Zufall et al., 2000) and olfactory neurons (Bhandawat et al., 2010) and marine sperm cells (Kaupp et al., 2003), are known to achieve a sensitivity high enough to detect a small number of molecules.

The sensitivity of a chemoreceptor is characterised by its binding affinity for the ligand, as well as its association/dissociation kinetics. If the time-scale of ligand dissociation is longer than the time-scale of the changes in ligand concentration, or if the ligands bind irreversibly, the sensitivity is determined by the binding rate alone. It has been shown that the theoretical limit of sensing accuracy is achieved when the receptors detect the frequency of binding events and when re-binding is excluded (Bialek and Setayeshgar, 2005; Endres and Wingreen, 2009) Because diffusion is fast on very short length scales, only 1% of the surface area of a cell or cilium needs to be covered in high-affinity receptors to obtain near-perfect adsorption (Berg and Purcell, 1977). Even if this condition is not satisfied, the membrane itself could non-specifically bind the ligands with near-perfect efficacy, which then reach the receptors in a two-stage process. In either of these cases, as long as there is no advection, the binding rates can be estimated using the theory of diffusion-limited reactions (Adam and Delbruck, 1968). This binding rate is known as the diffusion limit, and it has already been shown that flagella-driven swimming microorganisms can break the diffusion limit in order to increase their access to nutrients (Short et al., 2006).

The increasingly overlapping functions of sensory and motile cilia lead to the natural question about the advantage of placing receptors on a cilium, or in particular on a motile cilium. Because of its small volume, a cilium forms a compartment that facilitates efficient accumulation of second messengers (Marshall and Nonaka, 2006; Hilgendorf et al., 2016). Placing receptors on a protrusion, away from the flat surface, could have other advantages, like avoiding the effect of surface charges or the glycocalycx. It has also been suggested that the location of chemoreceptors on cilia exposes them to fluid that is better mixed (Marshall and Nonaka, 2006). A recent study suggests that the hydrodynamic interaction between motile and sensory cilia can enhance the sensitivity of the latter (Reiten et al., 2017). However, the question of how the geometry and motility of cilia affect their ability to capture and detect ligands has still remained largely unexplored.

In this paper, we investigate the theoretical limits on association rates of ligands on passive and motile cilia. In particular, we address the question of whether the elongated shape of a cilium and its motility can improve its chemosensory effectiveness. By using analytical arguments and numerical simulations, we show that the capture rate of a cilium is significantly higher than that of a receptor located on a flat epithelial surface. Motile cilia can further improve their chemosensitivity. Finally, we show that a cilium within an immotile bundle has a lower capture rate than an isolated cilium, but a higher one when the cilia are sufficiently motile.

In this study, we calculate the second-order rate constant for diffusive particle capture on a cilium. We discuss scenarios where the fluid and the cilium are at rest, where the fluid exhibits a shear flow, where the cilium is actively beating, and where a bundle of hydrodynamically interacting cilia absorbs particles.

We consider a perfectly absorbing cilium protruding from a non-absorbing surface, in a fluid containing some chemical species with a concentration field $c$. Far from the cilium, the unperturbed concentration has a constant value ${\displaystyle c_0}$. The rate constant $k$ is defined such that

where $I$ is the capture rate, defined as the number of captured particles per unit time.

Since the aforementioned cilium is perfectly absorbing, we define an absorbing boundary condition such that the concentration of the chemical species is zero at every point on the cilium’s surface. We assume that the flat membrane surrounding the cilium does not absorb particles and it is therefore described with a reflecting boundary condition at $z=0$. The geometry and boundary conditions are illustrated in Figure 1.

Figure 1

Download asset Open asset

Cilium in quiescent fluid

We consider a cilium (modelled as a cylinder next to a boundary at $z=0$) in a quiescent fluid, with the goal of determining its capture rate constant in the absence of advection. In the case where there is a steady state with no advection, the advection-diffusion equation reduces to

(2) $D{\nabla }^{2}c=0,$

where $D$ is the diffusion constant. The rate constant is determined by the integral of the current density $\mathbf{𝐉}$ over the surface, which follows from Fick’s law:

(3) $k=-\frac{1}{c_0}\int d𝐒\cdot 𝐉=\frac{1}{c_0}\int d𝐒\cdot (D\nabla c).$

As we show in Appendix 1, the rate constant can be evaluated using an analogy between particle diffusion and electrostatics (Berg and Purcell, 1977). Up to a prefactor, the capture rate is determined by the self-capacitance $C$ of a conducting body of the same shape as $k=DC/{\epsilon }_0$.

To determine the capture rate of a cilium embedded in a non-absorbing surface, we first eliminate the reflective boundary condition at the surface by symmetrically extending the problem to a cylinder of length $2L$ in open space and considering $1/2$ of its capacitance. There is no closed-form expression for the capacitance of a cylinder, so we loosely approximate this cylinder as a prolate spheroid with semi-major axis $L$ and semi-minor axis $a$. Using its self-capacitance in the limit $L\gg a$ (Snow, 1954), we find the rate constant:

(4) $k_{\text{cilium}}=2\pi D\frac{L}{\ln \left(2L/a\right)}.$

This value agrees well with simulations: the ratio of the simulated to this analytical rate constant is 1.02.

The finding that the capture rate scales almost linearly with the length of the cilium can be compared to experimental data obtained on olfactory cilia from the nasal cavity of mouse, whose lengths in different regions vary from a few micrometers to tens of micrometers. Challis et al., 2015 have used patch-clamp recordings on olfactory sensory neurons and measured the response to pulses of an odorant (eugenol or a mixture of 10 odorants), lasting $5-400\mathrm{ms}$. Regions with different lengths show very different sensitivity thresholds, differing by an order of magnitude. The results are qualitatively consistent with the predicted length dependence of the capture rate.

To quantify the advantage of localising the receptors on a cilium, we compare it with a case where the receptors form a circular patch on a flat surface (Figure 2a). Again, we assume that the receptor patch has a perfectly absorbing surface, while the surface surrounding it is reflective. We determine the size of the patch needed to attain the same rate constant as the cilium. The rate constant for a circular patch on the reflective boundary can be found by applying the electrostatic analogy to the well-known result for the self-capacitance of a thin conducting disc of radius $R$ (Berg and Purcell, 1977):

(5) $k_{\text{patch}}\approx 4DR.$

Figure 2 with 1 supplement see all

Download asset Open asset

We find that the patch has a much larger surface area than the cilium with the same rate constant. We can calculate this area ratio:

(6) ${\displaystyle \frac{A_{\text{patch}}}{A_{\text{cilium}}}\approx \frac{{\pi }^2}{8}\cdot \frac{L}{a}\frac{1}{{\ln }^2(2L/a)}.}$

The area ratio as a function of the aspect ratio $L/a$ is shown in Figure 2b. For a typical cilium aspect ratio of $L/a=80$ (with ${\displaystyle L=10\mu \mathrm{m}}$, and ${\displaystyle a=125\mathrm{n}\mathrm{m}}$), this area ratio is 3.8, implying that the cilium is much more effective per unit area than a receptor on the surface of the cell. Olfactory cilia have a great variation of lengths, ranging from $2.5\mu \mathrm{m}$ to $100\mu \mathrm{m}$ (Challis et al., 2015; Williams et al., 2014). If we neglect the fact that long cilia are not straight, the calculated area ratio ranges from 2.7 to 18. Using an exact numerical result for the capacitance of a cylinder (Paffuti, 2018), the ratio becomes 4.5 for $L/a=80$. With the dimensions given above, the radius of the circular patch with the same capture rate is ${\displaystyle R=3.4\mu \mathrm{m}}$.

Active pumping

A mounting collection of evidence suggests that both primary and motile cilia have sensory roles (Bloodgood, 2010). We are interested in the extent to which cilium motility can increase their ability to detect particles. To this end, we numerically simulate various different possible types of ciliary motion in otherwise quiescent fluids.

Because of the complex flow patterns and time-dependent boundary conditions, the absorption by a beating cilium is not analytically tractable. Instead, we use numerical simulations to determine the rate constants. We consider four different active pumping scenarios: a purely reciprocally moving cilium, a cilium tracing out a cone around an axis perpendicular to the surface, a cilium tracing out a tilted cone, and a cilium with a trajectory that includes bending, to raise the pumping efficiency (all shown in their respective order in Figure 3a–d).

Figure 3 with 1 supplement see all

Download asset Open asset

Figure 3—source data 1

Event counts and calculated rates as shown in Figure 3e.

https://cdn.elifesciences.org/articles/66322/elife-66322-fig3-data1-v2.zip

Download elife-66322-fig3-data1-v2.zip

The rate constants in these scenarios, relative to that of a non-moving cilium, are plotted in Figure 3e. Analogously to the cilium in a shear flow, we define the Péclet number using the maximum tip velocity during the cycle:

(15) $\mathrm{Pe}=\frac{v_{\text{tip}}^{\text{max}}L}{D}.$

The reciprocally moving cilium (Figure 3a) displays almost no improvement over several orders of magnitude of the Péclet number. This is expected, because Purcell’s scallop theorem (Purcell, 1977) states that purely reciprocal motion does not create any net flow, so the particle intake is largely diffusive in nature. A minor increase of the rate constant with the Péclet number is caused by the local shear flow that facilitates absorption on the surface.

The cilium moving around a vertical cone (Figure 3b) induces a net rotational flow, but no inflow or outflow (by symmetry, the time-averaged flow can only have a rotational component [Vilfan, 2012]). Nevertheless, the constant motion of the cilium through the fluid leads to a higher local capture efficiency. The rate constant therefore shows more improvement; over a few orders of magnitude of the Péclet number, the rate constant increases by a factor of two.

The tilted cone (Figure 3c) shows a much higher capture rate, which is unsurprising. When the cilium is near to the plane, the no-slip boundary screens the flow, whereas when it is far from the plane, its pumping is unimpeded. This results in the cilium inducing a long range flow in one direction, characterised by a finite volume flow rate (Smith et al., 2008). The long range flow causes a constant intake that replenishes the depleted particles. At high Péclet numbers, the capture rate scales $k\sim {\mathrm{Pe}}^{1/3}$, which is the same dependence as in an external shear-flow, although with a prefactor that is lower by a factor of ∼2. Locally, the relative flow around the cilium is the same whether a cilium is pivoting or resting in a shear flow. The pumping effect of the tilted cilium, on the other hand, provides sufficient inflow that the concentration around a cilium sees only a small depletion effect.

We finally simulated the capture process on a cilium exerting a realistic beating pattern, consisting of a stretched working stroke and a bent, sweeping recovery stroke (Figure 3d). The capture rate is close to that of the tilted cone, but surpasses it at very high Péclet numbers.

Collective active pumping

We consider seven cilia on a hexagonal centred lattice with lattice constant $0.95L$, with a view to understand how the presence of multiple cilia affects performance. We quantify the performance gain using a quantity $Q$, which we define as

(16) $Q=\frac{k_{\text{multiple}}}{k_{\text{cilium}}(\mathrm{Pe})\cdot N_{\text{cilia}}},$

which represents the fractional per-cilium improvement in rate constant compared to a single isolated cilium at the same Péclet number.

Using numerical simulations, we find that at zero Péclet number (Figure 4a–b), $Q\approx 0.5$, which means that the cilia locally deplete the concentration field, harming the per-cilium effectiveness; in a quiescent fluid, it is most efficient for cilia to stand far away from their neighbours.

Figure 4

Download asset Open asset

Figure 4—source data 1

Event counts and calculated rates as shown in Figure 4h.

https://cdn.elifesciences.org/articles/66322/elife-66322-fig4-data1-v2.zip

Download elife-66322-fig4-data1-v2.zip

However, when the cilia actively move (with each tracing out a tilted cone with a different randomly-chosen phase lag compared to its neighbours, as in Figure 4e) the trend is reversed: we find that at $\mathrm{Pe}\approx 10000$, $Q\approx 1.53$, meaning that per cilium, the capture rate is around 50% higher in the collective when compared to an isolated cilium with the same Péclet number. We find that over the range of Péclet numbers simulated, the $Q$ increases monotonically with the Péclet number (Figure 4h).

When comparing these randomly chosen phases to a patch of cilia which beat in uniform, we find that cilia which beat in phase (Figure 4d) see an improvement over the stationary case with $Q\approx 1.16$, but are much less effective than the cilia patch that beats with random phases. The random phases give a higher volume flow, and complex hydrodynamic interactions between the randomly-phased cilia result in a slightly higher capture chance for any given particle. Similar levels of improvement are also seen for other arrangements of cilia forming a bundle, that is, $N_{\text{cilia}}=19$ on a hexagon (Figure 4f) or $N_{\text{cilia}}=4$ on a square (Figure 4g). Furthermore, an improvement of randomly beating over uniformly-beating cilia has been observed in previous work with finite-sized particles (Ding and Kanso, 2015; Nawroth et al., 2017). The results suggest that mutual enhancement of capture rates is a robust phenomenon and does not depend on a specific geometry.

Our results address a simple question: does the location of so many chemical receptors on cilia bring them an advantage in sensitivity? Besides the well-known advantages of compartmentalisation, which facilitates the downstream signal processing, we show that the elongated shape of a cilium provides an advantage for the capture rate of molecules in the surrounding fluid. The advantages can be summarised as follows:

1. If neither the fluid nor the cilium move and the process of particle capture is purely diffusive, the elongated shape improves the capture rate of the cilium by giving it better access to the diffusing ligands. The length dependence of the capture rate has the sub-linear form $k\sim L/\log L$. With typical parameters, the cilium achieves a capture rate equivalent to that of a circular patch of receptors on a flat surface with 4× the surface area of the cilium.

2. When a non-moving cilium is exposed to a shear flow, the advantage increases, mainly because the tip of the cilium is exposed to higher flow velocities. The capture rate scales with $k\sim L^{4/3}$ and becomes equivalent to that of a surface patch with approximately 6× the surface area at high flow rates.

3. An actively beating cilium can achieve capture rates comparable to those by a passive cilium in a shear flow with the same relative tip velocity, but only if the beating is non-reciprocal, that is, if the cilium generates a long range directed flow. The capture rate can scale with the beating frequency to the power of 1/3 or higher.

4. Without motility, a bundle of sensory cilia achieves a capture rate per cilium that is lower than that of a single cilium, because of the locally depleted ligand concentration. However, the situation can become reversed if the cilia are beating: then each cilium benefits from the flow produced by the bundle as a whole, and the per-cilium capture rate can be significantly higher than in an isolated beating cilium. Cilia beating with random phases achieve significantly higher capture rates than when beating in synchrony.

Our results are based on a few assumptions. We assumed that the particles get absorbed and detected upon their first encounter of the cilia surface – an assumption that is justified if the receptors are covering the surface at a sufficient density (Berg and Purcell, 1977), or if the particles bind non-specifically to the membrane of the cilium first. We also treat the particles as point-like (their size only has an influence on their diffusivity), which is accurate for molecules up to the sizes of a protein and we do not expect a significant error even for small vesicles. The Rotne-Prager tensor approximation used to determine the flow fields does not exactly satisfy the no-slip boundary condition on the surface of the cilium, especially at high Péclet numbers.

With the typical dimensions of a cilium ($L=10\mu \mathrm{m}$, $a=0.125\mu \mathrm{m}$) and a diffusion constant of a small molecule ${\displaystyle D={10}^{-9}{\mathrm{m}}^2{\mathrm{s}}^{-1}}$, we obtain $k_{\text{cilium}}=7{\mathrm{pM}}^{-1}{\mathrm{s}}^{-1}$. A chemosensory cilium working at the physical limit is therefore capable of detecting picomolar ligand concentrations on a timescale of seconds. Sensitivity thresholds in the sub-picomolar range have been measured in some olfactory neurons (Frings and Lindemann, 1990; Zhang et al., 2013), indicating that some olfactory cilia work close to the theoretical sensitivity limit. If the cilia are embedded in mucus with a viscosity at least 3 orders of magnitude higher than water (Lai et al., 2009) (we disregard its viscoelastic nature here) and the molecule has a Stokes radius of a few nanometres, the diffusion-limited capture rate reduces to around $k_{\text{cilium}}=1{\mathrm{nM}}^{-1}{\mathrm{s}}^{-1}$.

In a shear flow with a typical shear rate of ${\displaystyle \dot{\gamma }=10{\mathrm{s}}^{-1}}$, the Péclet number of a small molecule in water is of the order of $\mathrm{Pe}\approx 1$, where the capture rate still corresponds to the stationary case. However, with larger molecules and higher viscosities, the Péclet numbers can exceed 10⁴, leading to a significant enhancement of the capture rate.

When the same cilium is beating with a frequency of ${\displaystyle 25\mathrm{H}\mathrm{z}}$, the Péclet number is of the order ∼10, which is too small to have an effect on the capture rate. With larger molecules and higher viscosities, the Péclet numbers can be significantly higher. With a medium viscosity of ${\displaystyle 0.2\mathrm{P}\mathrm{a}\cdot \mathrm{s}}$ (200 times the water viscosity) and a Stokes radius of ${\displaystyle 10\mathrm{n}\mathrm{m}}$, it reaches 10⁵, meaning that the motility accelerates the capture rate by one order of magnitude. For example, according to one hypothesis, motile cilia in the zebrafish left-right organizer (Kupffer’s vesicle) both generate flow and detect signalling particles, possibly extracellular vesicles (Ferreira et al., 2017; Ferreira et al., 2019) similar to the proposed ‘nodal vesicular parcels’ (Tanaka et al., 2005). With a cilium length of $L=6\mu \mathrm{m}$ and a particle radius of $a=100\mathrm{nm}$, we obtain ${\displaystyle \mathrm{P}\mathrm{e}=1300}$, showing that the capture rates can be several times higher than in a passive cilium. Figure 5 shows how the molecular Stokes radius affects the fluid viscosity required to break the diffusion limit for a few different scenarios. However, when the particle size becomes comparable to the cilium diameter, the approximation that treats them as point particles loses validity. Indeed, it has been shown that particle size can have a direct steric effect on the capture rate (Ding and Kanso, 2015). Furthermore, the capture process of large particles can depend on a competition between hydrodynamic and adhesive forces (Tripathi et al., 2013). Steric effects can even lead to particle enrichment in flow compartments (Nawroth et al., 2017).

Figure 5

Download asset Open asset

We have thus proven that for individual isolated cilia the geometry of a cilium always means an advantage in chemical sensitivity over receptors covering the same area on a flat surface (assuming they act as perfect absorbers), whether in a quiescent or moving fluid. At high Péclet numbers, which are achieved in viscous fluids, with very large particles or in very strong flows, the advantage of a cilium increases further and even confers an advantage in chemosensitivity to cilium bundles over individual cilia. These advantages can work in concert with others, such as avoiding charged surfaces and glycocalix and the provision of a closed compartment on the inside. Further work might examine the extent to which motility benefits cilia in a fluid with bulk flow, or investigate the effect of metachronal waves on ciliary chemosensitivity. Finally, our results shed light on possible engineering applications for microfluidic sensing devices based on these ideas, for example using magnetic actuation (Vilfan et al., 2010; Meng et al., 2019; Matsunaga et al., 2019).

Numerically simulated point particles are injected into a finite system containing a motile cilium, and move around due to advection (resulting from the motion of the cilium) and diffusion, until they either escape from the system or are absorbed by the cilium. The proportion of particles which are captured is used to compute a rate constant.

Injection

Request a detailed protocol

We require a particle injection procedure that satisfies the concentration boundary condition $c\to c_0$ far from the absorbing cilium. We achieve this by introducing two bounding boxes in the simulation: an inner and an outer box, separated by a thin distance $d$ (Figure 6). The particles are injected at the boundary of the inner box and absorbed at the outer box. The injection rate is calculated such that it corresponds to the advective-diffusive flux through the layer between the boxes if the concentration at the inner box is ${\displaystyle c_0}$. Because the flux through the boundary layer is much larger than the flux of particles absorbed inside the inner box, the method is suited to ensure a constant concentration boundary condition. The method is similar to a recent algorithm using a single boundary (Ramírez-Piscina, 2018), but uses a simpler injection function.

Figure 6

Download asset Open asset

To calculate the injection current density, we solve the one-dimensional steady-state advection-diffusion equation

(19) $0=D\frac{{\mathrm{d}}^{2}c}{\mathrm{d}{x}^2}-v\frac{\mathrm{d}c}{\mathrm{d}x},$

with the boundary conditions $c(0)=0$ and $c(d)=c_0$. The solution is 

(20) $c(x)=c_0\frac{e^{\mathrm{vx}/\mathrm{D}}-1}{e^{\mathrm{vd}/\mathrm{D}}-1}.$

By the application of Fick’s law, this leads to an expression for the current density through the inner box:

(21) $j(x)=v{c}_0\frac{1}{1-e^{-\mathrm{vd}/\mathrm{D}}}.$

We assume that a test particle will take take much longer to reach the cilium than the characteristic time required for the flow to change, and hence we take ${\displaystyle v=⟨\mathbf{u}(\mathbf{x},t)\cdot \hat{\mathbf{n}}{⟩}_t}$, where ${\displaystyle \hat{\mathbf{n}}}$ is the inward pointing surface normal of the inner box. This function can then be used to probabilistically weight where particles are injected on the inner box.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 3 and 4.

1. Book

   1. Adam G
   2. Delbruck M

   (1968)

   Reduction of dimensionality in biological diffusion processes

   In: Rich A, Davidson N, editors. Structural Chemistry and Molecular Biology. San Francisco: W.H. Freeman. pp. 198–215.

   • Google Scholar
2. 1. Berbari NF
   2. O'Connor AK
   3. Haycraft CJ
   4. Yoder BK

   (2009) The primary cilium as a complex signaling center

   Current Biology 19:R526–R535.

   https://doi.org/10.1016/j.cub.2009.05.025

   • PubMed
   • Google Scholar
3. 1. Berg HC
   2. Purcell EM

   (1977) Physics of chemoreception

   Biophysical Journal 20:193–219.

   https://doi.org/10.1016/S0006-3495(77)85544-6

   • PubMed
   • Google Scholar
4. 1. Bhandawat V
   2. Reisert J
   3. Yau KW

   (2010) Signaling by olfactory receptor neurons near threshold

   PNAS 107:18682–18687.

   https://doi.org/10.1073/pnas.1004571107

   • PubMed
   • Google Scholar
5. 1. Bialek W
   2. Setayeshgar S

   (2005) Physical limits to biochemical signaling

   PNAS 102:10040–10045.

   https://doi.org/10.1073/pnas.0504321102

   • PubMed
   • Google Scholar
6. 1. Bloodgood RA

   (2010) Sensory reception is an attribute of both primary cilia and motile cilia

   Journal of Cell Science 123:505–509.

   https://doi.org/10.1242/jcs.066308

   • PubMed
   • Google Scholar
7. 1. Brooks ER
   2. Wallingford JB

   (2014) Multiciliated cells

   Current Biology 24:R973–R982.

   https://doi.org/10.1016/j.cub.2014.08.047

   • PubMed
   • Google Scholar
8. 1. Bustamante-Marin XM
   2. Ostrowski LE

   (2017) Cilia and mucociliary clearance

   Cold Spring Harbor Perspectives in Biology 9:a028241.

   https://doi.org/10.1101/cshperspect.a028241

   • PubMed
   • Google Scholar
9. 1. Challis RC
   2. Tian H
   3. Wang J
   4. He J
   5. Jiang J
   6. Chen X
   7. Yin W
   8. Connelly T
   9. Ma L
   10. Yu CR
   11. Pluznick JL
   12. Storm DR
   13. Huang L
   14. Zhao K
   15. Ma M

   (2015) An olfactory cilia pattern in the mammalian nose ensures high sensitivity to odors

   Current Biology 25:2503–2512.

   https://doi.org/10.1016/j.cub.2015.07.065

   • PubMed
   • Google Scholar
10. 1. Ding Y
    2. Kanso E

    (2015) Selective particle capture by asynchronously beating cilia

    Physics of Fluids 27:121902.

    https://doi.org/10.1063/1.4938558

    • Google Scholar
11. 1. Elgeti J
    2. Gompper G

    (2013) Emergence of metachronal waves in cilia arrays

    PNAS 110:4470–4475.

    https://doi.org/10.1073/pnas.1218869110

    • PubMed
    • Google Scholar
12. 1. Endres RG
    2. Wingreen NS

    (2009) Maximum likelihood and the single receptor

    Physical Review Letters 103:158101.

    https://doi.org/10.1103/PhysRevLett.103.158101

    • PubMed
    • Google Scholar
13. 1. Essner JJ
    2. Vogan KJ
    3. Wagner MK
    4. Tabin CJ
    5. Yost HJ
    6. Brueckner M

    (2002) Conserved function for embryonic nodal cilia

    Nature 418:37–38.

    https://doi.org/10.1038/418037a

    • PubMed
    • Google Scholar
14. 1. Faubel R
    2. Westendorf C
    3. Bodenschatz E
    4. Eichele G

    (2016) Cilia-based flow network in the brain ventricles

    Science 353:176–178.

    https://doi.org/10.1126/science.aae0450

    • PubMed
    • Google Scholar
15. 1. Ferreira RR
    2. Vilfan A
    3. Jülicher F
    4. Supatto W
    5. Vermot J

    (2017) Physical limits of flow sensing in the left-right organizer

    eLife 6:e25078.

    https://doi.org/10.7554/eLife.25078

    • PubMed
    • Google Scholar
16. 1. Ferreira RR
    2. Fukui H
    3. Chow R
    4. Vilfan A
    5. Vermot J

    (2019) The cilium as a force sensor-myth versus reality

    Journal of Cell Science 132:jcs213496.

    https://doi.org/10.1242/jcs.213496

    • PubMed
    • Google Scholar
17. 1. Friedlander SK

    (1957) Mass and heat transfer to single spheres and cylinders at low Reynolds numbers

    AIChE Journal 3:43–48.

    https://doi.org/10.1002/aic.690030109

    • Google Scholar
18. 1. Frings S
    2. Lindemann B

    (1990) Single unit recording from olfactory cilia

    Biophysical Journal 57:1091–1094.

    https://doi.org/10.1016/S0006-3495(90)82627-8

    • PubMed
    • Google Scholar
19. 1. Gilpin W
    2. Bull MS
    3. Prakash M

    (2020) The multiscale physics of cilia and flagella

    Nature Reviews Physics 2:74–88.

    https://doi.org/10.1038/s42254-019-0129-0

    • Google Scholar
20. 1. Golestanian R
    2. Yeomans JM
    3. Uchida N

    (2011) Hydrodynamic synchronization at low Reynolds number

    Soft Matter 7:3074.

    https://doi.org/10.1039/c0sm01121e

    • Google Scholar
21. 1. Guasto JS
    2. Rusconi R
    3. Stocker R

    (2012) Fluid mechanics of planktonic microorganisms

    Annual Review of Fluid Mechanics 44:373–400.

    https://doi.org/10.1146/annurev-fluid-120710-101156

    • Google Scholar
22. 1. Hilgendorf KI
    2. Johnson CT
    3. Jackson PK

    (2016) The primary cilium as a cellular receiver: organizing ciliary GPCR signaling

    Current Opinion in Cell Biology 39:84–92.

    https://doi.org/10.1016/j.ceb.2016.02.008

    • PubMed
    • Google Scholar
23. 1. Kaupp UB
    2. Solzin J
    3. Hildebrand E
    4. Brown JE
    5. Helbig A
    6. Hagen V
    7. Beyermann M
    8. Pampaloni F
    9. Weyand I

    (2003) The signal flow and motor response controling chemotaxis of sea urchin sperm

    Nature Cell Biology 5:109–117.

    https://doi.org/10.1038/ncb915

    • PubMed
    • Google Scholar
24. 1. Lai SK
    2. Wang YY
    3. Wirtz D
    4. Hanes J

    (2009) Micro- and macrorheology of mucus

    Advanced Drug Delivery Reviews 61:86–100.

    https://doi.org/10.1016/j.addr.2008.09.012

    • PubMed
    • Google Scholar
25. 1. Lee IT
    2. Nakayama T
    3. Wu CT
    4. Goltsev Y
    5. Jiang S
    6. Gall PA
    7. Liao CK
    8. Shih LC
    9. Schürch CM
    10. McIlwain DR
    11. Chu P
    12. Borchard NA
    13. Zarabanda D
    14. Dholakia SS
    15. Yang A
    16. Kim D
    17. Chen H
    18. Kanie T
    19. Lin CD
    20. Tsai MH
    21. Phillips KM
    22. Kim R
    23. Overdevest JB
    24. Tyler MA
    25. Yan CH
    26. Lin CF
    27. Lin YT
    28. Bau DT
    29. Tsay GJ
    30. Patel ZM
    31. Tsou YA
    32. Tzankov A
    33. Matter MS
    34. Tai CJ
    35. Yeh TH
    36. Hwang PH
    37. Nolan GP
    38. Nayak JV
    39. Jackson PK

    (2020) ACE2 localizes to the respiratory cilia and is not increased by ACE inhibitors or ARBs

    Nature Communications 11:5453.

    https://doi.org/10.1038/s41467-020-19145-6

    • PubMed
    • Google Scholar
26. 1. Leinders-Zufall T
    2. Lane AP
    3. Puche AC
    4. Ma W
    5. Novotny MV
    6. Shipley MT
    7. Zufall F

    (2000) Ultrasensitive pheromone detection by mammalian vomeronasal neurons

    Nature 405:792–796.

    https://doi.org/10.1038/35015572

    • PubMed
    • Google Scholar
27. 1. Lisicki M
    2. Velho Rodrigues MF
    3. Goldstein RE
    4. Lauga E

    (2019) Swimming eukaryotic microorganisms exhibit a universal speed distribution

    eLife 8:e44907.

    https://doi.org/10.7554/eLife.44907

    • PubMed
    • Google Scholar
28. 1. Malicki JJ
    2. Johnson CA

    (2017) The cilium: cellular antenna and central processing unit

    Trends in Cell Biology 27:126–140.

    https://doi.org/10.1016/j.tcb.2016.08.002

    • PubMed
    • Google Scholar
29. 1. Marshall WF
    2. Nonaka S

    (2006) Cilia: tuning in to the cell's antenna

    Current Biology 16:R604–R614.

    https://doi.org/10.1016/j.cub.2006.07.012

    • PubMed
    • Google Scholar
30. 1. Masoud H
    2. Stone HA

    (2019) The reciprocal theorem in fluid dynamics and transport phenomena

    Journal of Fluid Mechanics 879:P1.

    https://doi.org/10.1017/jfm.2019.553

    • Google Scholar
31. 1. Matsunaga D
    2. Hamilton JK
    3. Meng F
    4. Bukin N
    5. Martin EL
    6. Ogrin FY
    7. Yeomans JM
    8. Golestanian R

    (2019) Controlling collective rotational patterns of magnetic rotors

    Nature Communications 10:4696.

    https://doi.org/10.1038/s41467-019-12665-w

    • PubMed
    • Google Scholar
32. 1. Meng F
    2. Matsunaga D
    3. Yeomans JM
    4. Golestanian R

    (2019) Magnetically-actuated artificial cilium: a simple theoretical model

    Soft Matter 15:3864–3871.

    https://doi.org/10.1039/C8SM02561D

    • PubMed
    • Google Scholar
33. 1. Nachury MV
    2. Mick DU

    (2019) Establishing and regulating the composition of cilia for signal transduction

    Nature Reviews Molecular Cell Biology 20:389–405.

    https://doi.org/10.1038/s41580-019-0116-4

    • PubMed
    • Google Scholar
34. 1. Nawroth JC
    2. Guo H
    3. Koch E
    4. Heath-Heckman EAC
    5. Hermanson JC
    6. Ruby EG
    7. Dabiri JO
    8. Kanso E
    9. McFall-Ngai M

    (2017) Motile cilia create fluid-mechanical microhabitats for the active recruitment of the host microbiome

    PNAS 114:9510–9516.

    https://doi.org/10.1073/pnas.1706926114

    • PubMed
    • Google Scholar
35. Preprint

    1. Paffuti G

    (2018) Results for capacitances and forces in cylindrical systems

    arXiv.

    https://arxiv.org/abs/1801.08202

    • Google Scholar
36. 1. Purcell EM

    (1977) Life at low Reynolds number

    American Journal of Physics 45:3–11.

    https://doi.org/10.1119/1.10903

    • Google Scholar
37. 1. Ramírez-Piscina L

    (2018) Fixed-density boundary conditions in overdamped Langevin simulations of diffusion in channels

    Physical Review E 98:013302.

    https://doi.org/10.1103/PhysRevE.98.013302

    • PubMed
    • Google Scholar
38. 1. Reiten I
    2. Uslu FE
    3. Fore S
    4. Pelgrims R
    5. Ringers C
    6. Diaz Verdugo C
    7. Hoffman M
    8. Lal P
    9. Kawakami K
    10. Pekkan K
    11. Yaksi E
    12. Jurisch-Yaksi N

    (2017) Motile-cilia-mediated flow improves sensitivity and temporal resolution of olfactory computations

    Current Biology 27:166–174.

    https://doi.org/10.1016/j.cub.2016.11.036

    • PubMed
    • Google Scholar
39. 1. Shah AS
    2. Ben-Shahar Y
    3. Moninger TO
    4. Kline JN
    5. Welsh MJ

    (2009) Motile cilia of human airway epithelia are chemosensory

    Science 325:1131–1134.

    https://doi.org/10.1126/science.1173869

    • PubMed
    • Google Scholar
40. 1. Short MB
    2. Solari CA
    3. Ganguly S
    4. Powers TR
    5. Kessler JO
    6. Goldstein RE

    (2006) Flows driven by flagella of multicellular organisms enhance long-range molecular transport

    PNAS 103:8315–8319.

    https://doi.org/10.1073/pnas.0600566103

    • PubMed
    • Google Scholar
41. 1. Smith DJ
    2. Blake JR
    3. Gaffney EA

    (2008) Fluid mechanics of nodal flow due to embryonic primary cilia

    Journal of the Royal Society Interface 5:567–573.

    https://doi.org/10.1098/rsif.2007.1306

    • Google Scholar
42. Book

    1. Snow C

    (1954)

    Circular of the Bureau of Standards No. 544: Formulas for Computing Capacitance and Inductance

    US Government Printing Office.

    • Google Scholar
43. 1. Spasic M
    2. Jacobs CR

    (2017) Primary cilia: cell and molecular mechanosensors directing whole tissue function

    Seminars in Cell & Developmental Biology 71:42–52.

    https://doi.org/10.1016/j.semcdb.2017.08.036

    • PubMed
    • Google Scholar
44. 1. Spassky N
    2. Meunier A

    (2017) The development and functions of multiciliated epithelia

    Nature Reviews Molecular Cell Biology 18:423–436.

    https://doi.org/10.1038/nrm.2017.21

    • PubMed
    • Google Scholar
45. 1. Stone HA

    (1989) Heat/mass transfer from surface films to shear flows at arbitrary Peclet numbers

    Physics of Fluids A: Fluid Dynamics 1:1112–1122.

    https://doi.org/10.1063/1.857335

    • Google Scholar
46. 1. Tanaka Y
    2. Okada Y
    3. Hirokawa N

    (2005) FGF-induced vesicular release of sonic hedgehog and retinoic acid in leftward nodal flow is critical for left-right determination

    Nature 435:172–177.

    https://doi.org/10.1038/nature03494

    • PubMed
    • Google Scholar
47. 1. Tocino A
    2. Senosiain MJ

    (2015) Two-step Milstein schemes for stochastic differential equations

    Numerical Algorithms 69:643–665.

    https://doi.org/10.1007/s11075-014-9918-9

    • Google Scholar
48. 1. Tripathi A
    2. Bhattacharya A
    3. Balazs AC

    (2013) Size selectivity in artificial cilia-particle interactions: mimicking the behavior of suspension feeders

    Langmuir 29:4616–4621.

    https://doi.org/10.1021/la400318f

    • PubMed
    • Google Scholar
49. 1. Uchida N
    2. Golestanian R

    (2010) Synchronization and collective dynamics in a carpet of microfluidic rotors

    Physical Review Letters 104:178103.

    https://doi.org/10.1103/PhysRevLett.104.178103

    • PubMed
    • Google Scholar
50. 1. Vilfan M
    2. Potočnik A
    3. Kavčič B
    4. Osterman N
    5. Poberaj I
    6. Vilfan A
    7. Babič D

    (2010) Self-assembled artificial cilia

    PNAS 107:1844–1847.

    https://doi.org/10.1073/pnas.0906819106

    • PubMed
    • Google Scholar
51. 1. Vilfan A

    (2012) Generic flow profiles induced by a beating cilium

    The European Physical Journal E 35:72.

    https://doi.org/10.1140/epje/i2012-12072-3

    • Google Scholar
52. 1. Williams CL
    2. McIntyre JC
    3. Norris SR
    4. Jenkins PM
    5. Zhang L
    6. Pei Q
    7. Verhey K
    8. Martens JR

    (2014) Direct evidence for BBSome-associated intraflagellar transport reveals distinct properties of native mammalian cilia

    Nature Communications 5:5813.

    https://doi.org/10.1038/ncomms6813

    • PubMed
    • Google Scholar
53. 1. Zhang J
    2. Pacifico R
    3. Cawley D
    4. Feinstein P
    5. Bozza T

    (2013) Ultrasensitive detection of amines by a trace amine-associated receptor

    Journal of Neuroscience 33:3228–3239.

    https://doi.org/10.1523/JNEUROSCI.4299-12.2013

    • PubMed
    • Google Scholar
54. 1. Zimmermann KW

    (1898) Beiträge zur Kenntniss einiger Drüsen und Epithelien

    Archiv für Mikroskopische Anatomie 52:552–706.

    https://doi.org/10.1007/BF02975837

    • Google Scholar

Author details

1. David Hickey

   Max Planck Institute for Dynamics and Self-Organization (MPIDS), Göttingen, Germany

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0149-712X

2. Andrej Vilfan

   1. Max Planck Institute for Dynamics and Self-Organization (MPIDS), Göttingen, Germany
   2. J. Stefan Institute, Ljubljana, Slovenia

   Contribution

   Conceptualization, Software, Formal analysis, Supervision, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   andrej.vilfan@ds.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8985-6072

3. Ramin Golestanian

   1. Max Planck Institute for Dynamics and Self-Organization (MPIDS), Göttingen, Germany
   2. Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   For correspondence

   Ramin.Golestanian@ds.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3149-4002

• 950

  Page views

• 189

  Downloads

• 7

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.