Abstract
Mucus clearance constitutes the primary defence of the respiratory system against viruses, bacteria and environmental insults. This transport across the entire airway emerges from the integrated activity of thousands of multiciliated cells, each containing hundreds of cilia, which together must coordinate their spatial arrangement, alignment and motility. The mechanisms of fluid transport have been studied extensively at the level of an individual cilium, collectively moving metachronal waves and, more generally, the hydrodynamics of active matter. However, the connection between local cilia architecture and the topology of the flows they generate remains largely unexplored. Here, we image the mouse airway from subcellular (nm) to organ (mm) scales, characterizing quantitatively its ciliary arrangement and the generated flows. Locally, we measure heterogeneity in both cilia organization and flow structure, but, across the trachea, fluid transport is coherent. To examine this result, a hydrodynamic model was developed for a systematic exploration of different tissue architectures. Surprisingly, we find that disorder enhances particle clearance, whether it originates from fluctuations, heterogeneity in multiciliated cell arrangement or ciliary misalignment. This resembles elements of ‘stochastic resonance’, in the sense that noise can improve the function of the system. Taken together, our results shed light on how the microstructure of an active carpet determines its emergent dynamics. Furthermore, this work is also directly applicable to human airway pathologies, which are the third leading cause of deaths worldwide.
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Data availability
The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.
Code availability
The computer codes used in this paper are available from the corresponding author upon reasonable request.
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Acknowledgements
This work was supported by funding from the National Science Foundation Center for Cellular Construction (NSF grant no. DBI-1548297), a Human Frontier Science Program Fellowship to A.J.T.M.M. (LT001670/2017) and a Ruth L. Kirschstein National Research Service Award to M.H. (F32HD089639). M.P. acknowledges support from the Keck Foundation. We thank K. Anderson (Sloan Kettering Institute) for providing the Arl13b-mCherry/Centrin-GFP animals used for this study and the Stanford Research Computing Center for providing computational resources and support. We thank W. Gilpin for providing comments on the manuscript.
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G.R.R.-S.J., W.M. and M.P. designed the research. G.R.R.-S.J. and M.H. performed the tissue imaging. G.R.R.-S.J. analysed the data. A.J.T.M.M. contributed intellectually to the paper and developed the simulations. G.R.R.-S.J. and A.J.T.M.M. wrote the manuscript. M.H. and L.J. provided key reagents and resources for tissue imaging. All authors edited the final manuscript.
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Extended data
Extended Data Fig. 1 Quantification of spatial patterning in airway ciliary arrays.
A. Immunofluorescence image of a trachea expressing Centrin-GFP (left). Regions outlined by the squares are magnified to the right, showing that multiciliated cells form a ‘patchwork’ pattern in the distal (1), medial (2) and proximal (3) regions of the trachea. B. Illustration of cilia orientation analysis. Individual cilia orientation is measured from pairs of Centrin and Centriolin images. First, individual basal bodies (crossed symbols) are identified from Centrin images. Then the cross-correlation function for a pair of Centrin-Centriolin images is calculated in windows of 50 pixels centered around each basal body. The direction of maximum correlation corresponds to the orientation of each cilium (white arrows). C. Heatmap scaled such the 1 corresponds to the regions of maximum cilia activity in Supp. Video 1. Right: Binary image where white regions show regions where cilia are active in Supp. Video 1 (See Methods §2 b). D. Coverage fraction and wavelength measured by calculating the spatial correlation function of binary images where regions with cilia activity were identified (e.g. image shown in C).
Extended Data Fig. 2 Quantification of flows generated by airway ciliary arrays.
A. Transmitted light image of trachea showing the scale at which flow is measured. Right: Longitudinal flow strength (vx, red-blue) generated by trachea pictured left. Entire flow strength and streamlines shown in (Fig. 2A,C). B. Representative transmitted light image of a typical region of the tissue where flow microstructure is analysed. Right: Longitudinal flow strength (vx, red-blue) generated by trachea pictured left. Entire flow strength and streamlines shown in (Fig. 2A,C; right). C. Quantification of beat frequency and tip velocity of cilia in multiciliated cells. Kymographs drawn from the lines shown in DIC image. Each peak corresponds to one cilia beat cycle (blue), while the slopes of the line indicate the rate at which cilia tips move over time (green). Boxplot central marks indicate the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers. D. Plot of temporal correlations of the flow field. Plot on the right shows the region where the δCvv(τ) decays, magnified. Each trace is the correlation function M9calculated for a field of view of the size shown in B. Points correspond to values measured, solid line shows exponential fits with an oscillatory component (See Methods §2 d). E. Plot of spatial correlations of flow fields measured experimentally. Points correspond to values calculated, solid lines show fits (See Methods §2 d). Right: Heatmap of the two dimensional spatial autocorrelation function \({C}_{vv}(\bf{R})\) for the flow field shown in Fig. 2C (right panel). F. Plot of spatial correlations of simulated flow fields. Measurements of multiciliated cell organisation were used as an input for these simulations (See Methods §3 e). Right: Heatmap of the two dimensional spatial autocorrelation function \({C}_{vv}(\bf{R})\) for the simulated flow field shown in Fig. 2D (right panel).
Extended Data Fig. 3 Effect of confinement on Stokeslet flow in a liquid film.
Shown is the analytical solution using 9 image reflections, (n) = (0) − (9) in Table 1 of Mathijssen et al. [34]. The film height in the z direction is H, compared to the horizontal scale x, y ∈ [− 1, 1]. The Stokeslets are located in the middle of the film, at z = H/2, and oriented in the x direction (white arrows). Top panels: Magnitude (thermal) and streamlines (black arrows) of the flow velocity. Bottom panels: Same, showing the x component (red-blue) of the flow. For thin films a recirculation emerges, with a vortex centre marked in green.
Extended Data Fig. 4 Three-dimensional structure of the flow generated by a ciliary array.
Three-dimensional structure of the flow generated by a ciliary array, simulated for a square lattice with coverage fraction ϕ = 0.1, patchiness λ/H = 12.8, crystallinity γ = 1 and aligned cilia 〈px〉 = 1. The wavelength λ = L = 128μm and the film height H = 10μm. Shown are streamlines (white) and the longitudinal flow strength (red-blue). Left. Top view showing the z-averaged flow velocity, \(\bar{\bf{v}}\), which is the same as Fig. 3D4. Upper right. Cross-section at y = 0 or y = L, between the ciliated cells. Lower right. Cross-section at y = L/2, above the ciliated cells.
Extended Data Fig. 5 Effect of shear-dependent viscosity on particle clearance.
A. Non-Newtonian viscosity μ as a function of shear rate \(\dot{\gamma }\) in a power-law fluid (Eq. M19), for a shear-thinning liquid (blue, n = 0.5), a Newtonian liquid (green, n = 1) and a shear-thickening liquid (red, n = 2). B. Channel flow velocity profiles for a power-law fluid. Symbols indicate simulated flows with the CFD solver and lines show the theoretical prediction. For a shear-thinning fluid (blue) the profile is flatter, for a shear-thickening fluid it is sharper (red), and for a Newtonian liquid we recover a parabolic Poiseuille flow. C. Flow generated by a ciliary array for different values of the power-law exponent, n, simulated for a square lattice with coverage fraction ϕ = 0.1, patchiness λ/H = 12.8, crystallinity γ = 1 and aligned cilia 〈px〉 = 1. Shown are streamlines (white) and the longitudinal flow strength \({\bar{v}}_{x}\) normalised with respect to the mean flow \(\langle {\bar{v}}_{x}\rangle\) of a Newtonian fluid (red-blue), as in Fig. 3D4. D. Plot of total flux and clearance time for a shear-thinning (blue) and a shear-thickening fluid (red) as a function of patchiness, normalised with respect to the case of homogeneous coverage (λ = 0). E. Plot of total flux and clearance time for a shear-thinning (blue) and a shear-thickening fluid (red) as a function of Péclet number, normalised with respect to the case of weak noise (Pé = 10−4).
Extended Data Fig. 6 Total flux and particle clearance for a hexagonal array of ciliary patches.
The lattice is shown in the inset of panel D, where red indicates a multiciliated cell. A. Simulated flow for cilia oriented in the \(\hat{\bf{x}}\) direction. Shown are streamlines (white) and the longitudinal flow strength (red-blue). The coverage fraction is ϕ = 0.33, the patchiness is λ/H = 12.8, the crystallinity γ = 1 and the cilia are all aligned 〈px〉 = 1. B. Same, for cilia oriented in the θ = π/6 direction. C. Same, for cilia oriented in the \(\hat{\bf{y}}\) direction. D. Plot of total flux (blue) and clearance time (red) as a function of patchiness, for cilia oriented in the \(\hat{\bf{y}}\) direction, similar to Fig. 3D. E. Plot of total flux (blue) and clearance time (red) as a function of Péclet number, for cilia oriented in the \(\hat{\bf{y}}\) direction, similar to Fig. 4C.
Extended Data Fig. 7 From the literature: SEM images of airway multiciliated tissue.
A. Chicken; Image adapted from46. B. Dog; Image adapted from47. C. Pig; Image adapted from48. D. Human; Image adapted from49. E. Ferret; Image adapted from50. F. Rat; Image adapted from51. G. Snake; Scale bar=100 μm. Image adapted from52. H. Rabbit; Image adapted from53. I. Hamster; Image adapted from54. Scale bar=10 μm for all panels except G.
Supplementary information
Supplementary Information
Extended materials and methods.
Supplementary Video 1
Activity of a field of multiciliated cells in the trachea. Time-lapse ex-vivo DIC imaging of a section of the mouse trachea multiciliated tissue.
Supplementary Video 2
Ciliary driven flows are globally coherent. Flow generated by multiciliated cells across the entire trachea. Flow is imaged by time-lapse confocal microscopy. Fluorescent beads serve as tracer particles.
Supplementary Video 3
Ciliary driven flows are locally heterogeneous. Microstructure of the flow generated by a subsection of the trachea multiciliated tissue. Flow is imaged by time-lapse confocal microscopy. Fluorescent beads serve as tracer particles.
Supplementary Video 4
Activity of multiciliated cells in the trachea.Time-lapse ex-vivo DIC imaging of a cross-section of the mouse trachea multiciliated tissue.
Supplementary Video 5
Fluctuations improve particle clearance in patchy arrangements of multiciliated cells. Particle trajectories (Np = 1,000) are simulated for a range of noise strengths. Initially, the particles are distributed evenly and identically in all three panels, emulating a uniform deposition from breathed-in air. Over time, they are subject to flow (same as in Fig. 4c) and fluctuations with different diffusion constants, such that the Péclet number is Pé = 104, 103, 102, respectively. Colours indicate particle index and black trails show their pathlines. For weak noise the particles accumulate in recirculation zones, but strong fluctuations facilitate a faster clearance.
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Ramirez-San Juan, G.R., Mathijssen, A.J.T.M., He, M. et al. Multi-scale spatial heterogeneity enhances particle clearance in airway ciliary arrays. Nat. Phys. 16, 958–964 (2020). https://doi.org/10.1038/s41567-020-0923-8
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DOI: https://doi.org/10.1038/s41567-020-0923-8
