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Modelling and Analysis of Chevron Formation in the Fish Myotome

Authors:

Abstract

About 10 hours after fertilization of the zebrafish egg the first somites begin to appear (at 28.5°C). The boundary surfaces between the newly formed somites are planar. During the following 3 hours one observes that the boundaries change their shape into so-called chevrons. Simultaneously, the muscle cells form the myotome and finally connect pairs of somite boundaries [1]. So far, the only suggested mechanism for chevron formation in zebrafish relates swimming movements to somite boundary changes [2]. We review this hypothesis and search for an alternative mechanism which also accounts for chevron formation in non-moving mutants. We analysed movies of developing zebrafish embryos [3] to quantify the boundary shape changes. We suggest that they are due to the forces generated by the developing muscle cells. In a first mathematical model we minimize the energy of a chain of coupled springs. Model analysis yields the exponential increase in boundary bending as a function of somite index which has been observed for the anterior somites. We furthermore outline the application of coupled amplitude equations as a mathematical model for chevron formation in trunk somites. The aim of our ongoing work is to predict how observed shapes emerge and change under modified conditions. [1] Kimmel, C. B., Ballard, W. W., Kimmel, S. R., Ullmann, B., & Schilling, T. F. (1995). Stages of embryonic development of the zebrafish. Dev. Dyn., 203, 253-310. [2] Van Raamsdonk, W., Mos, W., Tekronnie, G., Pool, C., & Mijzen, P. (1979). Differentiation of the musculature of the teleost brachydanio-rerio. 2. effects of immobilization on the shape and structure of somites. Acta Morphologica Neerlando-Scandinavica, 17, 259-274. [3] Schröter, C., Herrgen, L., Cardona, A., Brouhard, G. J., Feldman, B., & Oates, A. C. (2008). Dynamics of zebrafish somitogenesis. Dev. Dyn., 237, 545-553.
Modelling and Analysis of
Chevron Formation in the Fish Myotome
Fabian Rost1
fabian.rost@zih.tu-dresden.de
Lutz Brusch1
lutz.brusch@tu-dresden.de
Andrew C. Oates2
oates@mpi-cbg.de
1Dresden University of Technology, Centre of Information Services and High Performance Computing, 01062 Dresden, Germany
2Max Planck Institue of Molecular Cell Biology, Pfotenhauerstr. 108, 01307 Dresden, Germany
Introduction
Myotomes of adult teleost have a folded shape often referred to as
chevron shape. The chevron shape has been proposed to be optimal
for the alternating body bending during swimming. Much less is
known about which processes underly the ontogenetic development
of the myotome shape.
As far as we know, the only study that deals with this topic was
done by Van Raamsdonk et al. (1974, 1977, 1979). They conclude
”that the lateral body movements have both a shape determining
and a shape-stabilizing role during the early stages of somite mor-
phogenesis.”
We aim to shed new light on the mechanisms that shape the my-
otome chevrons by developing and analysing a mathematical model
of the interactions between muscle fibres and somite boundaries.
3D shape of a myotome of a 3 weeks old zebrafish larva, from Van
Raamsdonk (1979).
Confocal micrographs of β-catenin stained 17 hpf zebrafish
embryo. Side view, anterior to the left, somites 15-19. Red:
Somite boundaries, blue: elongating cells, cyan: cells spanning the
somite. Modified from Henry (2004).
Development of chevrons in the zebrafish myotome. From left to right: 10 somite stage: somite boundaries straight, anterior boundaries begin to
fold. 26 somite stage: most anterior boundaries fairly straight, trunk somites have constant angle, newly formed posterior somites begin folding.
Larva shortly before hatching. Camera Lucida sketches, scale bar = 250µm, taken from zebrafish stageing series from Kimmel (1995).
Model Framework
a1a2a3
x3
x2
x2
Kx
KxKa
Kx
Ka
KxKxKx
L L L
We minimize the energy of a chain of coupled springs:
E=1
2
n
X
i=1
Kxx2
i+Kaa2
i
L=xi+aiai1, i = 1 . . . n
Model analysis yields the exponential increase in
boundary bending as a function of somite index:
ai=C1λi
1+C2λi
2
0
2
4
6
8
10
5 10 15 20 25 30
ai
Somite Number
Ka/Kx:
0.01
0.1
1
10
Data Analysis
We measured angle βin light field time-lapse movies of
dechorionated zebrafish embryos using ImageJ. We calculated
α= 90 β
2. The movies were provided by Christian Schroeter.
0
10
20
30
40
120 160 200
α [´]
Time [min]
Somite 20
Somite 21
Somite 22
-10
0
10
20
30
40
50
0 10 20 30
α [´]
Somite Number i
Zebrafish
Xenopus
Top: Lateral view of a stage 29/30 xenopus embryo expressing
Dvl1 in the somites. From Gray et al. (2009). Bottom: Lateral
view of a 27-somites stage zebrafish embryo. From Schroeter et
al. (2008).
Model Variant: Variable Spring Constants
Constant chevron angles for all somites can be obtained for
a=K1
xK2
x
K1
a+K1
x
=K2
xK3
x
Ka
=· · · =Ki
xKi+1
x
Ki
a
=· · · =Kn
x
Kn
a
.
.
0
0.5
1
1.5
2
2.5
3
3.5
0 5 10 15 20 25 30
Kx/Ka
Somite Number
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25 30
ai
Somite Number
Solution of the equation above with Ki
a=const,a= 0.1.
Linear increasing angle ai=m·ican be reproduced with
m=K2
xK1
x
K2
xK1
xK1
a
=K3
xK2
x
K3
xK2
x2K2
a
=· · · =Ki+1
xKi
x
Ki+1
xKi
xiKi
a
=· · · =Kn
x
Kn
x+nKn
a
.
0
10
20
30
40
50
5 10 15 20 25 30
Kx/Ka
Somite Number
0
0.02
0.04
0.06
0.08
0.1
0 5 10 15 20 25 30
ai
Somite Number
m=1/300
Model Variant: Anharmonic Energy
Introducing anharmonic springs leads to different somite angles. For in-
stance E=1
2Pn
i=1 Kxx2
i+Kae2aican lead to
5
10
15
20
25
30
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
.
We aim to find a potential that leads to a linear increase in ai.
Conclusions
Although our model is not yet able to completely predict the correct
chevron angles in zebrafish we believe our outlined extensions will be able
to do so. The current hypothesis that elongating muscle pioneers which
exert forces on the somite boundaries and therefore are responsible for
the chevron formation is compatible with our model. Data from mutants
with defect chevron or mechanically perturbed embryos would help to
validate our model furthermore.
References
Gray, R.S. et al. Diversification of the expression patterns and developmental functions of the dishevelled gene family during chordate evolution. Dev.
Dyn. 238, 2044-2057 (2009).
Kimmel, C. et al. Stages of embryonic development of the zebrafish. Dev. Dyn. 203, 253-310 (1995).
Raamsdonk, W. et al. Differentiation of the musculature of the teleost Brachydanio rerio. Anat. and Embr. 145, 321-342 (1974).
Raamsdonk, W. et al. Differentiation of the Musculature of the Teleost Brachydanio-Rerio. 2. Effects of Immobilization on the Shape and Structure of
Somites. Acta Mor. Neerl.-Scand. 17, 259-274 (1979).
Raamsdonk, W. et al. On the relation between movements and the shape of the somites in early embryos of the teleost Brachydanio rerio. Contrib. Zool
46, 261-274 (1977).
Schroeter, C. et al. Dynamics of zebrafish somitogenesis. Dev. Dyn. 237, 545-553 (2008).
... Zebrafish muscle segments take on a similar folded shape as the muscle segments in the regenerating axolotl tail. Previously, I proposed a mechanism involving cells that exert mechanical forces to organize the observed segment shapes (Rost 2010). Here, I quantify the segment shapes to test whether the proposed cellular behavior could drive the shaping of muscle tissue. ...
... Contrary to previous suggestions, I show that a particular cell type, the muscle pioneer, is not required for initial shape formation. Finally, I test the tension-and-resistance mechanism that I proposed in my diploma thesis (Rost 2010). This mechanism involves interactions between intra-segmental tension, possibly due to a particular cell differentiation pattern, and segment boundaries. ...
... In particular, I show that muscle pioneers are not required for chevron formation. I test the tension-and-resistance mechanism that I proposed previously (Rost 2010). This mechanism involves interactions between intra-segmental tension and segment boundaries. ...
Thesis
Full-text available
The cell is the central functional unit of life. Cell behaviors, such as cell division, movements, differentiation, cell death as well as cell shape and size changes, determine how tissues change shape and grow during regeneration and development. However, a generally applicable framework to measure and describe the behavior of the multitude of cells in a developing tissue is still lacking. Furthermore, the specific contribution of individual cell behaviors, and how exactly these cell behaviors collectively lead to the morphogenesis and growth of tissues are not clear for many developmental and regenerative processes. A promising strategy to fill these gaps is the continuing effort of making developmental biology a quantitative science. Recent advances in methods, especially in imaging, enable measurements of cell behaviors and tissue shapes in unprecedented detail and accuracy. Consequently, formalizing hypotheses in terms of mathematical models to obtain testable quantitative predictions is emerging as a powerful tool. Tests of the hypotheses involve the comparison of model predictions to experimentally observed data. The available data is often noisy and based on only few samples. Hence, this comparison of data and model predictions often requires very careful use of statistical inference methods. If one chooses this quantitative approach, the challenges are the choice of observables, i.e. what to measure, and the design of appropriate data-driven models to answer relevant questions. In this thesis, I applied this data-driven modeling approach to vertebrate morphogenesis, growth and regeneration. In particular, I study spinal cord and muscle regeneration in axolotl, muscle development in zebrafish, and neuron development and maintenance in the adult human brain. To do so, I analyzed images to quantify cell behaviors and tissue shapes. 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I reanalyzed published data using the published deterministic neuron turnover model but I extended the model by a better justified measurement error model. Most importantly, I found that human adult neurogenesis might occur at an even higher rate than currently believed. The tools I used throughout were (1) the careful quantification of the involved processes, mainly by image analysis, and (2) the derivation and application of mathematical models designed to integrate the data through (3) statistical inference. Mathematical models were used for different purposes such as estimating unknown parameters from indirect experiments, summarizing datasets with a few meaningful parameters, formalizing mechanistic hypotheses, as well as for model-guided experimental planning. I venture an outlook on how additional open questions regarding cell turnover measurements could be answered using my approach. 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