The morphology and mechanical sensitivity of lateral line receptors in zebrafish larvae (Danio rerio)

The detection of water flow by the fish lateral line system influences behaviors as varied as spawning (Satou et al., 1994), obstacle detection(Hassan, 1986) and rheotaxis(Montgomery et al., 1997) (for reviews, see Bleckmann, 1994; Mogdans and Bleckmann, 2001; Coombs and van Netten, 2006). The lateral line detects flow with morphologically diverse receptors, called neuromasts, that are distributed over the surface of the body. Although it is clear that the shape of a neuromast greatly determines its mechanics(van Netten and Kroese, 1987)(McHenry et al., in press), it is unknown how natural variation in morphology affects how the lateral line detects flow. Therefore, we examined how neuromast morphology affects its mechanical sensitivity in zebrafish larvae (Danio rerio) by measurement and mathematical modeling.

Two types of neuromast can be distinguished by morphological differences. Superficial neuromasts are directly exposed to flow over the body and canal neuromasts are recessed within channels beneath the scales. Both types include a cluster of hair cells in the epithelium with kinocilia that extend into the water. The kinocilia and a surrounding gelatinous matrix form the cupula of the neuromast. The cupulae of canal neuromasts are generally hemispherical with a diameter of hundreds of micrometers. Superficial neuromasts have elongated cupulae that are an order of magnitude smaller in diameter(Münz, 1989)(Fig. 1). In both, water flow causes the cupula to deflect, which is transduced into graded receptor potentials in the hair cells by bundles of stereocilia that are linked to the kinocilia (Gillespie and Walker,2001). Therefore, the neurophysiological response of a neuromast depends on the degree to which cupular mechanics permit the deflection of the kinocilia in response to water flow. The relationship between cupular deflection and receptor potentials has been demonstrated in the ruffe(Acerina cernua L.), where physiological recordings of hair cell potentials closely matched recorded mechanical deflections up to 300Hz(Kroese and van Netten,1989).

Differences in the morphology of canal neuromasts create differences in mechanical sensitivity. Direct recordings of neuromast deflection using laser interferometry found that the frequency response of the canal neuromasts of the ruffe (Acerina cernua) exhibits a peak sensitivity around 116Hz,and the smaller neuromasts of the African knife fish (Xenomystus nigri) are most sensitive at 460 Hz(Wiersinga-Post and van Netten,2000). According to a mathematical model of their mechanics, this difference in peak sensitivity is due entirely to the discrepancy in cupula size (van Netten and Kroese,1987; Wiersinga-Post and van Netten, 2000).

A recently developed mathematical model examines the effects of morphology on the sensitivity of superficial neuromasts(McHenry et al., in press). This model treats the structure of the cupula as two beams joined end-to-end that are excited by a pressure-driven oscillatory boundary layer. It predicts the response of a neuromast by calculating the cupular deflections over a range of stimulus frequencies. This model suggests that the dimensions of the cupula dictate the generation of hydrodynamic forces and thereby affect the cupular deflections that determine neuromast sensitivity(McHenry et al., in press). The present study employs this model as a basis for interpreting how cupular morphology affects neuromast sensitivity in the superficial neuromasts of zebrafish larvae.

Zebrafish larvae are an excellent system for the study of superficial neuromasts. As in other species (Blaxter and Fuiman, 1989), the lateral line at the larval stage includes only a small number of neuromasts, which are similar to the superficial neuromasts of adult fish (Münz,1989; Webb and Shirey,2003). Additionally, almost all of the 31 neuromasts on each side of the body are easily visualized with transmitted illumination because of the transparent bodies of the larvae (McHenry and van Netten, 2007). Finally, the lateral line of zebrafish larvae has become a focus of investigation on vertebrate hair cell mechanotransduction (e.g. Sidi et al.,2003; Corey et al.,2004) and regeneration (e.g. Harris et al., 2003; Ma et al., 2008). Therefore,understanding the morphological basis of sensitivity in this model system has the potential to offer insight on the physiology of vertebrate hair cells.

Zebrafish larvae were raised with standard culturing techniques. A breeding colony of wild-type (AB line) zebrafish (Danio rerio, Hamilton 1922)was housed in a flow-through tank system (Aquatic Habitats, Apopka, FL, USA)that was maintained at 28.5°C on a 14 h: 10 h light–dark cycle. The fertilized eggs from randomized mating were cultured according to standard protocols (Westerfield, 1993)and larvae were raised in an incubator in E3 embryo media(Brand et al., 2002). Morphological measurements were made from the cupulae of neuromasts in larvae that were between 3 and 20 days post fertilization (d.p.f.). Larvae were anesthetized for these measurements in a solution of 0.0017 g l^–1 MS-222 (tricaine methanesulfonate; Finquel Inc., Argent Chemical Laboratories Inc., Redmond, WA, USA) and embryo media buffered with Tris to a pH of ∼7. All measurements were conducted on live larvae, the health of which we assessed by monitoring blood flow throughout the study.

The results of a pilot study suggested that cupular morphology changes during hatching and immediately afterward. We therefore examined cupular morphology in two groups. In the first group, variation in cupular morphology was assessed in larvae between 5 and 20 d.p.f. In the second, we examined the consequences of hatching by comparing measurements between hatched larvae at 3 or 4 d.p.f. with unhatched larvae at 3 d.p.f. that were extracted by tearing open the chorion with forceps.

Cupulae were visualized by coating their surface with polystyrene microspheres. A concentrated solution of these particles (0.1 μm in diameter; Polysciences Inc., Warrington, PA, USA) was injected by syringe around the body of an anesthetized larva. Once coated, the periphery of the cupular matrix was visible under differential interference contrast optics. This approach was recently developed(McHenry and van Netten, 2007)as a means of avoiding the shrinkage (Cahn and Shaw, 1962; Blaxter,1984a; Rouse and Pickles,1991; Higgs and Fuiman,1998) and destruction (Webb,1989; Webb and Shirey,2003; Carton and Montgomery,2004; Gibbs and Northcutt,2004; Faucher et al.,2006) of cupulae that had accompanied previous visualization techniques. After coating the cupulae, larvae were mounted in a 0.3% agarose solution in embryo media and 0.0017 g l^–1 MS-222 within a deep-welled glass slide. Individual neuromasts were observed with a ×40 water-immersion objective with an additional stage of ×10 magnification. The location of each neuromast was determined using the conventions established by Harris and colleagues(Harris et al., 2003), which combined prior labeling practices for cranial(Raible and Kruse, 2000) and trunk (Metcalfe et al., 1985)neuromasts (Fig. 1).

The coordinates of morphological landmarks were measured with a custom-designed technique of 3D micromorphometrics. Neuromasts were visualized with a fixed-stage compound microscope (Zeiss Axioskop 2 FS plus, Carl Zeiss Microimaging Inc., Thornwood, NY, USA) mounted onto a translating base(MT-1078, Sutter Instrument Co., Novato, CA, USA). This setup allowed an investigator to locate neuromasts on the body of a stationary larva while the microscope was translated with three degrees of freedom (d.f.). Upon locating a neuromast, the locations of morphological landmarks were selected from photographs (640 pixels × 480 pixels, 8-bit monochromic; Fire-i Digital Board Camera, Unibrain Inc., San Ramon, CA, USA) of the microscope field of view (Fig. 2Ai). These landmarks were found in 3D space through the use of a custom-designed program in Matlab (v. 7.4 with video acquisition toolbox; Mathworks, Natick, MA, USA). This program prompts the user to select 2D coordinates of landmarks within the photographs. These coordinates are defined with respect to a local coordinate system (x_local and y_local) having an origin at the top of the circular field of view within the photograph(Fig. 2Aii,Aiii). For each photo, the program prompts the user for the focus setting on the microscope(z_global) and the position of the origin of the local coordinate system from a reading of the micrometers that actuate the translation base of the microscope (x_global and y_global). These coordinates were used to find the position of photographs within a global frame of reference that was fixed with respect to the body of the larva. The program then calculated the 3D position of landmarks in the global frame of reference as the vector sum of the origin of the local system and the coordinates within the local system(Fig. 2Aiv). We defined the central axis of the body as a vector between the anterior tip of the rostrum and posterior margin of the tail fin (Fig. 2B). All coordinates were transformed with respect to this axis to calculate the body position of landmarks (see McHenry and Lauder, 2006).

We recorded the coordinates of as many as seven landmarks from each neuromast. Three landmarks defined the centerline of a cupula by recording the middle of the cupula at the base, the distal tip of the longest kinocilium and the distal tip of the cupular matrix. The margins of the cupula were recorded at its base (where the cupula joins the surface of the sensory hillock) and in the middle (radially outward from the distal tip of the longest kinocilium;see dots on Fig. 2Aiv). The height of the cupula (h_c) and kinocilia(h_k) was calculated from the distance between center points. The diameter of the cupula at its base (d_b) and at the kinocilia tips (d_k) was calculated from the periphery of the cupula at these two heights (Fig. 2C). The accuracy of measurements (at the 95% level) was verified to 1 μm precision by performing the coordinate acquisition on micrometer scales of known length and variable orientation.

Statistical tests were used to assess whether the morphology and predicted sensitivity of neuromasts varied with body position or age. An analysis of variance (ANOVA) was used to determine whether morphological parameters, the peak amplitude of sensitivity and cut-off frequency were dependent upon either body position or age. Differences between neuromast locations were explored with a post-hoc analysis of location using the Bonferroni method to adjust for multiple comparisons (Sokal and Rohlf, 1995). This method conducts t-test pair-wise comparisons between each group in the ANOVA, adjusting the level of significance (α) such that α=0.05/K where K is the number of comparisons. When comparing larvae of different ages, mean values for each larva were used and groups (by day of development) were compared post-hoc using Tukey's least significant difference procedure (Sokal and Rohlf,1995). This procedure determines the minimum that is significant and determines whether each comparison exceeds that difference. Finally,coefficients of determination were calculated to assess the proportion of variability in frequency response explained by each morphological character. All statistical tests were preformed in Matlab (v. 7.4 with the statistics toolbox).

We successfully measured the morphology of nearly all neuromasts in the lateral line system. Measurements were conducted on a total of 495 neuromasts from 37 larvae. Some neuromasts were easier to locate than others, which created differences in sampling among loci(Fig. 4). Overall, a large number of measurements were generated for cupula height (N=386),kinocilia height (N=354), and the cupula diameter at the base(N=492) and kinocilia tips (N=274). The first two neuromasts of the infraorbital (IO) line were obscured by the eyes and consequently were the only cupulae for which there were no measurements.

Cupular morphology exhibited a broad range of variation among lateral line neuromasts. The values for cupula height spanned a 9-fold range (8.7 μm to 79.1 μm, Fig. 5A). Both kinocilia height (6.7 μm to 34.6μm, Fig. 5B) and cupula diameter(4.2μm to 25.1μm at kinocilia tips, Fig. 5C; 3.7 μm to 21.8μm at the base, Fig. 5D)spanned more than a 5-fold range. On average, the heights of cupulae (mean± 1 s.d., 40±14 μm; N=386) were around twice that of the kinocilia (mean ± 1 s.d., 20±4μm; N=354) and four times the cupular diameter at the base (mean ± 1 s.d., 11±3μm; N=492). The diameter at the kinocilia tips (mean ± 1 s.d., 11±3 μm; N=274) was not significantly different from the diameter at the cupular base (Student's unpaired t-test, P=0.056).

Under the assumptions of our model, variation in cupular morphology was predicted to create large differences in the frequency responses of neuromasts. Peak amplitude values spanned a 38-fold range(5.7×10^–4 to 220×10^–4; Fig. 5E) and cut-off frequencies spanned more than a 200-fold range (0.90 to 200 Hz) among all neuromasts (Fig. 5F). The form of these differences is revealed by the predicted frequency responses of neuromasts (Fig. 6). All neuromasts behaved as velocity detectors with low-pass filtering. They exhibited a nearly flat response (2 dB decade^–1) in the amplitude of sensitivity to local flow velocity up to the cut-off frequency. Although the form of the frequency response is similar in all neuromasts,sensitivity varied greatly at low frequencies due to morphological differences(Fig. 6A). At the lowest frequencies, the near-zero phase of sensitivity indicates that the cupulae deflect nearly synchronously with the velocity of flow close to the body(Fig. 6A). However, the transition in phase with frequency differed broadly due to the influence of morphology on cut-off frequency. At frequencies above the cut-off, amplitude attenuates at a rate of 17 dB decade^–1 and a phase lag around 75° emerges in all neuromasts, irrespective of morphological variation. Beyond 200 Hz, our model suggests that all neuromasts exhibited similar mechanical sensitivities. In total, we found that a large variation in mechanical response was predicted among neuromasts within individual fish(Fig. 6B) and at particular loci among individuals (Fig. 6C).

The results of our mathematical modeling suggest that cupular height is the dominant parameter in determining the frequency response of a neuromast. This result was formulated by first examining correlations between morphological parameters, cut-off frequency and peak amplitude (blue dots in Fig. 7). For each correlation,we considered the effect of the independent variable by running a series of simulations that differed only in values for the independent variable and maintained all other parameters at their mean measured values (green lines in Fig. 7). Finally, we calculated a coefficient of determination [r²(Sokal and Rolf, 1995)] that evaluated the proportion of variation in the correlations that were predicted by the independent variable. We found that most variation in peak amplitude(r²=0.76) and cut-off frequency(r²=0.90) may be attributed to variation in cupular height(Fig. 7A). Other parameters individually accounted for only as much as 15%(Fig. 7D) of the variation in peak amplitude and 8% of the variation in cut-off frequency(Fig. 7B,C).

Our findings suggest that variation in cupular morphology and frequency response is not related to the body position of a neuromast. Our ANOVA found that all morphological parameters and cut-off frequency, but not peak sensitivity, depended on neuromast location. ANOVA d.f. among locations was 28 for every parameter and ranged from 199 for peak sensitivity and 331 for cupula width at the base within locations. However, our post-hocanalysis (d.f. ranged from 0 for IO5 × P13 to 50 for MI1 × MI2 for cupula width at the base) found that the neuromasts at most loci were indistinguishable when significance levels were properly adjusted using the Bonferroni method. This is illustrated in Table 1 where two neuromasts that do not share a group are significantly different. For example, all but two neuromasts (SO1 and IO5) were indistinguishable by cupular height within a single group (`a' in h<sub>c</sub>, Table 1). The two neuromasts outside this group were not outliers in cupular height because they were still indistinguishable from most other neuromasts (groups `b' and `c' in h_c, Table 1). A similar lack of distinction was found among all morphometric parameters, cut-off frequency and peak amplitude(Table 1). Values of peak amplitude were particularly homogeneous, as all loci were found to belong to the same statistical group.

Cupulae exhibited significant differences in morphology and frequency response during the first days of larval development. Newly hatched larvae had shorter cupulae (Fig. 8A) and kinocilia (Fig. 8B) than unhatched larvae of the same age (3 d.p.f.). For all tested parameters ANOVA d.f. was 3 among age groups and 23 within age groups. The d.f. for our post-hoc analysis ranged from 6 for 4 d.p.f. × 3 d.p.f. and 16 for 3 d.p.f. × 5–20 d.p.f. We observed that the shorter cupulae of hatched larvae frequently exhibited an irregularly notched edge instead of the tapered tip that was common to longer cupulae. Among hatched larvae, long and tapered cupula were more common after the 1 or 2 days of growth that followed hatching. Cupulae recovered after hatching but never again achieved embryonic height values (Fig. 8A). Kinocilia, however, recovered to prehatching lengths within 1 day(Fig. 8B). The diameter of the cupula was not significantly different between hatched and prehatched larvae(Fig. 8C,D). Cbhanges in height were predicted to cause a significant increase in cut-off frequency and reduction in peak amplitude with hatching(Fig. 8E,F). Although cut-off frequency attained the prehatching level after hatching(Fig. 8F), peak sensitivity was higher in prehatching larvae than in any subsequent stage sampled(Fig. 8E).

Our results suggest that the sensitivity of lateral line neuromasts is greatly affected by the height of the cupulae. In zebrafish larvae, height is the most variable aspect of cupular morphology(Fig. 5) and it has the largest effect on frequency response (McHenry et al, in press). Under the assumptions of our modeling, these factors cause cupular height to generate 90% of the variation in cut-off frequency and 76% of the variation in peak amplitude among neuromasts(Fig. 7A). This suggests that cupular height is the major determinant of mechanical sensitivity in the lateral line system of zebrafish larvae.

These results are consistent with previous research on Mexican blind cavefish (Astyanax mexicanus). Teyke(Teyke, 1990) proposed that the taller cupulae (up to 300μm) of the blind morphotype creates a lateral line system with heightened sensitivity compared with that of their sighted relatives (up to 42μm). Greater height presents more surface area and exposes the neuromast to more rapid flow to create larger bending moments at the hair bundles (Teyke, 1990)(McHenry et al., in press). This probably contributes to the blind morphotype's ability to distinguish stationary surfaces without the aid of touch or sight(Weissert and von Campenhausen,1981; von Campenhausen et al.,1981). Therefore, the morphology and behavior of Mexican blind cavefish is consistent with our prediction that lateral line sensitivity is modulated by cupular height.

Our measurements provide indirect evidence that cupular height varies greatly because they are frequently damaged. The cupulae of larvae that hatched through the chorion were about half the height of unhatched larvae(Fig. 8A). The distal margins of many cupulae possessed irregularly notched edges in post-hatched larvae that contrasted with the smooth edges of cupulae from unhatched larvae. The notched edges provide evidence of damage from breaking through the chorion during hatching, as found in another cyprinid species (Gnathopogon elongates) (Mukai, 1995)and in herring (Clupea harengus)(Blaxter and Fuiman, 1989). We found that larvae began to recover their cupulae immediately following hatching, but never attained the height of prehatching fish(Fig. 8). If larvae persistently secrete the mucopolysaccharide material that composes the matrix(Blaxter, 1984a; Blaxter, 1984b; Mukai and Kobayashi, 1992),then cupular height may be regulated by the continuous growth and wear of the delicate cupular matrix (McHenry and van Netten, 2007). This suggests that damage caused by incidental contact with the environment and hydrodynamic forces may create high variability in cupular height throughout the larval stage (Figs 5 and 8).

We found that the large variation in cupular height does not follow a consistent pattern with body position (Fig. 5, Table 1). In contrast with reports on other species, this lack of a morphological pattern suggests that no region of the body is consistently more sensitive than the others (Fig. 5F). The cupulae of both adult Mexican blind cavefish [A. mexicanus(Teyke, 1990)] and larval glass knife fish [Eigenmannia sp.(Vischer, 1989)] are taller at anterior body positions. This pattern suggests the cranial region of the fish is more sensitive than the trunk. Given the few species for which cupular height has been reported, it is unclear whether sensitivity in superficial neuromasts typically varies with body position among fishes.

Our predictions of frequency response have implications for the dynamic range of a neuromast. The range of stimulus intensities that a neuromast may detect is dictated by the physiology of its hair cells. For a weak stimulus,detection requires that the kinocilia of the hair cells exceed a deflection threshold. At high intensities, the hair cells may saturate if the kinocilia deflections are too great (Hudspeth,1989). For intensities within these extremes, the relationship between deflection and the receptor potentials of a neuromast is anticipated to reflect the sigmoidal curve (Fig. 9) that is characteristic of individual hair cells(Hudspeth and Corey, 1977). The dynamic range of a neuromast may be defined as the range of flow velocities over which differences in velocity may be detected. As demonstrated in canal neuromasts (Kroese and van Netten, 1989), dynamic range is largely determined by cupular mechanics. These mechanics dictate how much the kinocilia within a neuromast deflect for a given flow stimulus. Our results suggest that the greater mechanical sensitivity of tall superficial neuromasts creates a smaller dynamic range.

The inverse relationship between cupular height and dynamic range has implications for the flow velocities that may be detected by the lateral line system. The dynamic range for the entire system may be defined as the range of intensities that can be detected among all neuromasts. Variable cupular morphology should generate a wide range of mechanical sensitivities among the neuromasts and thereby cause the system to be sensitive to a broad range of flow velocities (Fig. 9). Tall cupulae in the system would provide high sensitivity, but saturate at relatively low flow velocities. Short cupulae would be relatively insensitive,but encode stimuli of high intensity. Therefore, the dynamic range of a system of variable neuromast morphologies (Fig. 9Bii,Cii) may be much greater than would be possible if all neuromasts were uniform (Fig. 9Bi,Ci).

The role of neuromast morphology in flow sensing is mediated by the neurophysiology of the lateral line system. The receptor potentials generated by neuromast hair cells are encoded as a train of action potentials within afferent neurons (Dijkgraaf,1963). This encoding and its integration at the central nervous system filters signals beyond the mechanical filtering of the neuromasts. Integration begins in the afferent neurons, which may innervate multiple neuromasts within a section of the lateral line(Teyke, 1990; Ledent, 2002) and thereby average the responses of a group of receptors. Our findings suggest that an afferent neuron that innervates a group of neuromasts with taller cupulae will be more sensitive, and have a lower cut-off frequency and a smaller dynamic range than a neuron that innervates a group of short neuromasts. It is unlikely that such differences between neurons exist in zebrafish larvae because cupular heights do not correlate with body position(Fig. 5). The averaging of inputs by afferent neurons may help to explain why the frequency responses of afferent nerves are similar in some species despite variation in neuromast morphology (Coombs and Montgomery,1992; Coombs and Montgomery,1994; Montgomery et al.,1994).

Our findings suggest that the lateral line system of larval fish serves a functional role that is distinct from that of adults. Adult fish are large compared with prey that function as a stimulus source. This difference in scale allows adult fish to detect spatial patterns in flow along their body. It is thought that adult fish are capable of sensing stimulus direction and proximity by analyzing sensory cues from spatial variation in pressure gradients along the trunk (Coombs and Conley, 1997; Ćurčič-Blake and van Netten, 2006). Larvae, however, are much smaller than the flow generated by predators (Higham et al.,2006) and are therefore capable of sampling only a small portion of spatial gradients in flow. Zebrafish larvae are anticipated to have further difficulty in sensing flow gradients because of the high variability in the frequency responses predicted among neuromasts(Fig. 6). The variability in neuromast sensitivity that may assist in detecting a wide range of flow velocities (Fig. 9) may also hinder an ability to sense spatial cues. Therefore, the central nervous system of fish may process flow signals differently at different stages of their life history as a consequence of changes in body size relative to stimuli.

Katherine Yip assisted with data collection throughout the study. James Strother and members of the UCI biomechanics group provided thoughtful comments and insight on the project and earlier versions of this manuscript. This research was supported by National Science Foundation grants to M.J.M.(IOS-0723288 and IOB-0509740).