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First published online June 13, 2008
Journal of Experimental Biology 211, 2105-2115 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.016204
The morphology and mechanical sensitivity of lateral line receptors in zebrafish larvae (Danio rerio)
Department of Ecology and Evolutionary Biology, 5205 McGaugh Hall, University of California at Irvine, Irvine, CA 92697-2525, USA
* Author for correspondence (e-mail: wvantrum{at}uci.edu)
Accepted 21 April 2008
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Summary |
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 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Key words: fish, mechanosensory, hair cells, lateral line
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INTRODUCTION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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), 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).
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). 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.
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MATERIALS AND METHODS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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)
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.
Morphometrics
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 x40
water-immersion objective with an additional stage of x10 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 x 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 (xlocal and ylocal) 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
(zglobal) 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 (xglobal and
yglobal). 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).
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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 (hc) and kinocilia (hk) was calculated from the distance between center points. The diameter of the cupula at its base (db) and at the kinocilia tips (dk) 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.
Mathematical modeling
We used a mathematical model to calculate the frequency responses of
neuromasts from their cupular dimensions
(Fig. 3). This model treats the
stimulus as an oscillatory pressure field that generates a boundary layer of
flow over the surface of a fish's body
(McHenry et al., in press). If
the body is modeled as a flat plate, the velocity U within the
boundary layer varies with distance z normal to the surface, as
described by the following equation
(Batchelor, 1967):
 | (1) |
is the boundary layer thickness
[=(2µ/
)0.5], U
is
the freestream velocity, is the angular speed, and and µ are
the density and dynamic viscosity of freshwater, respectively. The model
treats the cupula as two beams joined end-to-end. Assuming small deflections
(<10% of cupula height), the motion of each beam may be calculated with the
following general equation (McHenry et
al., in press):
 | (2) |
(2a2–4µki–µk/L),
k=L/[L2+(/4)2],
L=
+ln[a(2)–0.5], E
is Young's modulus, I is the second moment of area,
Cj is a sequence of four integration constants, a
is the radius of the beam and is Euler's constant. The general
equation assumes that the density of the cupula is equal to that of the
surrounding water.
|
):
 | (3) |
):
 | (4) |
)]. The flexural stiffness of the proximal region,
(EI)prox, may be calculated by the following relationship:
 | (5) |
)], n is the number of hair cells and
Iprox is the second moment of area for the proximal
cupula.
Specific solutions to this equation require definitions for the boundary
conditions at the two ends of each beam within the cupula. At the tip of the
distal beam, it may be assumed that zero bending moment
and shearing force
are generated. At the junction between the beams, the two may be assumed to be
equal in deflection
[
prox(hk)=dist(0)],
orientation
,
bending moment
,
and shear force
(McHenry et al., in press).
Finally, the cupula may be assumed to be pinned at the base
[prox(0)=0] with the hair bundles acting as a torsion spring
that resists changes in orientation, as defined by:
 | (6) |
)]. We used the values of n reported by Harris and
colleagues for each neuromast locus
(Harris et al., 2003). These
boundary conditions define eight simultaneous linear equations that were used
to numerically solve for the four integration constants
(Eqn 2) for each of the two
beams. This calculation was performed within Matlab after defining all
parameter values to yield a specific solution to the model.
We calculated a frequency response of cupular deflection from specific
solutions to the model. Deflections were normalized by the stimulus intensity
(i.e. flow velocity) to provide a measure of sensitivity. Therefore, the
sensitivity S of a neuromast was calculated as:
 | (7) |
)]. This measure
of sensitivity is a complex number with a modulus equal to the amplitude
(Fig. 3B) and a phase
calculated from its argument as: 180°arg(S)/
(Fig. 3C). To find the
frequency response, we calculated sensitivity from specific solutions to the
model for hundreds of frequencies for 0.001 Hz<f<1000 Hz. For
each frequency response, we calculated the cut-off frequency and peak
amplitude from the relationship between frequency and the amplitude of
sensitivity. This was achieved by first finding a least-squares linear curve
fit in the 0.001 Hz<f<0.1 Hz range and a second line
constrained to a slope of –20 dB decade–1 at 10
Hz<f<1000 Hz. Cut-off frequency was taken as the frequency of
the intersection between these lines and peak amplitude was calculated as the
amplitude of the intersection (Fig.
3B).
Statistics
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 (
) such that =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).
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RESULTS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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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 (5.7x10–4 to 220x10–4; 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).
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)] 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).
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|
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. x 3 d.p.f. and 16 for 3 d.p.f. x 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).
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DISCUSSION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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). 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;
ur
i-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.
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Acknowledgments |
|---|
|
References |
|---|
|
TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
|---|
Batchelor, G. K. (1967). An Introduction to Fluid Dynamics. New York: Cambridge University Press.
Blaxter, J. H. S. (1984a). Cupular growth in herring neuromasts. J. Mar. Biol. Assoc. U.K. 64,935 -938.
Blaxter, J. H. S. (1984b). Neuromasts and cupular growth of cod larvae. Flodevigen Rapportserie 1, 183-188.
Blaxter, J. H. S. and Fuiman, L. A. (1989). Function of the free neuromasts of marine teleost larvae. In The Mechanosensory Lateral Line: Neurobiology and Evolution (ed. S. Coombs, P. Görner and H. Münz), pp.481 -499. New York: Springer-Verlag.
Bleckmann, H. (1994). Reception of hydrodynamic stimuli in aquatic and semiaquatic animals. Prog. Zool. 41,1 -115.
Brand, M., Granato, M. and Nuesslein-Volhard, C. (2002). Keeping and raising zebrafish. In Zebrafish (ed. C. Nusslein-Volhard and R. Dahm), pp.7 -38. Oxford: Oxford University Press.
Cahn, P. H. and Shaw, E. (1962). The first demonstration of lateral line cupulae in the mugiliformes. Copeia 1962,109 -114.[CrossRef]
Carton, A. G. and Montgomery, J. C. (2004). A comparison of lateral line morphology of blue cod and torrentfish: two sandperches of the family Pinguipedidae. Environ. Biol. Fishes 70,123 -131.[CrossRef]
Coombs, S. and Conley, R. A. (1997). Dipole source localization by mottled sculpin. 1. Approach strategies. J. Comp. Physiol. A 180,387 -399.[CrossRef][Medline]
Coombs, S. and Montgomery, J. (1992). Fibers innervating different parts of the lateral line system of antarctic notothenioid, Trematomus bernacchii, have similar frequency responses, despite large variation in the peripheral morphology. Brain Behav. Evol. 40,217 -233.[Medline]
Coombs, S. and Montgomery, J. (1994). Function and evolution of superficial neuromasts in Antarctic notothenoid fish. Brain Behav. Evol. 44,287 -298.[Medline]
Coombs, S. and van Netten, S. M. (2006). The hydrodynamics and structural mechanics of the lateral line system. In Fish Biomechanics (ed. R. E. Shadwick and G. V. Lauder), pp. 103-139. New York: Elsevier.
Corey, D. P., Garcia-Anoveros, J., Holt, J. R., Kwan, K. Y., Lin, S. Y., Vollrath, M. A., Amalfitano, A., Cheung, E. L. M., Derfler, B. H., Duggan, A. et al. (2004). TRPA1 is a candidate for the mechanosensitive transduction channel of vertebrate hair cells. Nature 432,723 -730.[CrossRef][Medline]
uri-Blake, B. and van Netten, S. M.
(2006). Source location encoding in the fish lateral line canal.
J. Exp. Biol. 209,1548
-1559.
Dijkgraaf, S. (1963). The functioning and significance of the lateral-line organs. Biol. Rev. 38, 51-105.[Medline]
Dinklo, T. (2005). Mechano- and electrophysiological studies on cochlear hair cells and superficial lateral line cupulae. PhD thesis, University of Groningen, The Netherlands.
Faucher, K., Fichet, D., Miramand, P. and Lagardere, J. P. (2006). Impact of acute cadmium exposure on the trunk lateral line neuromasts and consequences on the "C-start" response behaviour of the sea bass (Dicentrarchus labrax L.; Teleostei, Moronidae). Aquat. Toxical. 76,278 -294.[CrossRef]
Gere, J. M. (2001). Mechanics of Materials. Cheltenham: Nelson Thornes.
Gibbs, M. A. and Northcutt, R. G. (2004). Development of the lateral line system in the shovelnose sturgeon. Brain Behav. Evol. 64,70 -84.[CrossRef][Medline]
Gillespie, P. G. and Walker, R. G. (2001). Molecular basis of mechanosensory transduction. Nature 413,194 -202.[CrossRef][Medline]
Harris, J. A., Cheng, A. G., Cunningham, L. L., MacDonald, G., Raible, D. W. and Rubel, E. W. (2003). Neomycin-induced hair cell death and rapid regeneration in the lateral line of zebrafish (Danio rerio). J. Assoc. Res. Otolaryngol. 4, 219-234.[CrossRef][Medline]
Hassan, E. S. (1986). On the discrimination of spatial intervals by the blind cave fish (Anoptichthys jordani). J. Comp. Physiol. A 159,701 -710.[CrossRef][Medline]
Higgs, D. M. and Fuiman, L. A. (1998). Associations between sensory development and ecology in three species of clupeoid fish. Copeia 1,133 -144.
Higham, T. E., Day, S. W. and Wainwright, P. C.
(2006). Multidimensional analysis of suction feeding performance
in fishes: fluid speed, acceleration, strike accuracy and the ingested volume
of water. J. Exp. Biol.
209,2713
-2725.
Hudspeth, A. J. (1989). How the ear's works work. Nature 341,397 -404.[CrossRef][Medline]
Hudspeth, A. J. and Corey, D. P. (1977).
Sensitivity, polarity, and conductance change in the response of vertebrate
hair cells to controlled mechanical stimuli. Proc. Natl. Acad. Sci.
USA 74,2407
-2411.
Kroese, A. B. A. and van Netten, S. M. (1989). Sensory transduction in lateral line hair cells. In The Mechanosensory Lateral Line: Neurobiology and Evolution (ed. S. Coombs, P. Gorner and H. Munz), pp. 265-284. New York: Springer-Verlag.
Ledent, V. (2002). Postembryonic development of
the posterior lateral line in zebrafish. Development
129,597
-604.
Ma, E. Y., Rubel, E. W. and Raible, E. W.
(2008). Notch signaling regulates the extent of hair cell
regeneration in the zebrafish lateral line. J.
Neurosci. 28,2261
-2273.
McHenry, M. J. and Lauder, G. V. (2006). Ontogeny in form and function: locomotor morphology and drag in zebrafish (Danio rerio). J. Morphol. 267,1099 -1109.[CrossRef][Medline]
McHenry, M. J. and van Netten, S. M. (2007).
The flexural stiffness of superficial neuromasts in the zebrafish (Danio
rerio) lateral line. J. Exp. Biol.
210,4244
-4253.
McHenry, M. J., Strother, J. A. and van Netten, S. M. (2008). Mechanical filtering by the boundary layer and fluid-structure interaction in the superficial neuromast of the fish lateral line system. J. Comp. Physiol. A (in press).
Metcalfe, W. K., Kimmel, C. B. and Schabtach, E. (1985). Anatomy of the posterior lateral line system in young larvae of the zebrafish. J. Comp. Neurol. 233,377 -389.[CrossRef][Medline]
Mogdans, J. and Bleckmann, H. (2001). The mechnosensory lateral line of jawed fishes. In Sensory Biology of Jawed Fishes (ed. B. G. Kapoor and T. J. Hara), pp.181 -213. Enfield, NH: Science Publishers.
Montgomery, J., Coombs, S. and Janssen, J. (1994). Form and function relationships in lateral line systems: comparative data from six species of Antarctic notothenioid fish. Brain Behav. Evol. 44,299 -306.[Medline]
Montgomery, J. C., Baker, C. F. and Carton, A. G. (1997). The lateral line can mediate rheotaxis in fish. Nature 389,960 -963.[CrossRef]
Mukai, Y. (1995). Cupular growth of embryonic free neuromasts in the Willow Shiner Gnathopogon Elongatus Caerulescens and the pattern in the changes in cupular length after hatching. Fish. Sci. 61,521 -522.
Mukai, Y. and Kobayashi, H. (1992). Cupular growth-rate of free neuromasts in 3 species of cyprinid fish. Nippon Suisan Gakkai Shi 58,1849 -1853.
Münz, H. (1989). Functional organization of the lateral line periphery. In The Mechanosensory Lateral Line (ed. S. Coombs, P. Görner and H. Münz), pp.285 -298. Berlin: Springer-Verlag.
Raible, D. W. and Kruse, G. J. (2000). Organization of the lateral line system in embryonic zebrafish. J. Comp. Neurol. 421,189 -198.[CrossRef][Medline]
Rouse, G. W. and Pickles, J. O. (1991). Ultrastructure of free neuromasts of Bathygobius fuscus (Gobiidea) and canal neuromasts of Apogon cyanosoma (Apogonidea). J. Morphol. 209,111 -120.[CrossRef]
Satou, M., Takeuchi, H. A., Tanabe, M., Kitamura, S., Okumoto, N., Iwata, M. and Nishii, J. (1994). Behavioral and electrophysiological evidences that the lateral-line is involved in the inter-sexual vibrational communication of the hime salmon (landlocked red salmon, Oncorhynchus-nerka). J. Comp. Physiol. A 174,539 -549.
Sidi, S., Friedrich, R. W. and Nicolson, T.
(2003). NompC TRP channel required for vertebrate sensory hair
cell mechanotransduction. Science
301, 96-99.
Sokal, R. R. and Rohlf, F. J. (1995). Biometry. New York: W. H. Freeman.
Teyke, T. (1990). Morphological differences in neuromasts of the blind cave fish Astyanax hubbsi and the sighted river fish Astyanax mexicanus. Brain Behav. Evol. 35, 23-30.[Medline]
van Netten, S. M. and Kroese, A. B. A. (1987). Laser interferometric measurements on the dynamic behavior of the cupula in the fish lateral line. Hear. Res. 29, 55-61.[CrossRef][Medline]
Vischer, H. A. (1989). The development of lateral-line receptors in Eigenmannia (Teleostei, Gymnotiformes). 1. The mechanoreceptive lateral-line system. Brain Behav. Evol. 33,205 -222.[Medline]
von Campenhausen, C., Reiss, I. and Weissert, R. (1981). Detection of stationary objects by the blind cave fish Anoptichthys jordani (Characidae). J. Comp. Physiol. A 143,369 -374.[CrossRef]
Webb, J. F. (1989). Neuromast morphology and lateral line trunk canal ontogeny in two species of cichlids: an SEM study. J. Morphol. 202,53 -68.[CrossRef][Medline]
Webb, J. F. and Shirey, J. E. (2003). Postembryonic development of the cranial lateral line canals and neuromasts in zebrafish. Dev. Dyn. 228,370 -385.[CrossRef][Medline]
Weissert, R. and von Campenhausen, C. (1981). Discrimination between stationary objects by the blind cave fish Anoptichthys jordani (Characidae). J. Comp. Physiol. A 143,375 -381.[CrossRef]
Westerfield, M. (1993). The zebrafish Book: A Guide for the Laboratory use of Zebrafish (Brachydanio rerio). Eugene, OR: University of Oregon Press.
Wiersinga-Post, J. E. C. and van Netten, S. M. (2000). Temperature dependency of cupular mechanics and hair cell frequency selectivity in the fish canal lateral line organ. J. Comp. Physiol. A 186,949 -956.[CrossRef][Medline]
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