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2016 ; ä (110
): e53921
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Analysis of Zebrafish Kidney Development with Time-lapse Imaging Using a
Dissecting Microscope Equipped for Optical Sectioning
#MMPMID27078207
Perner B
; Schnerwitzki D
; Graf M
; Englert C
J Vis Exp
2016[Apr]; ä (110
): e53921
PMID27078207
show ga
In order to understand organogenesis, the spatial and temporal alterations that
occur during development of tissues need to be recorded. The method described
here allows time-lapse analysis of normal and impaired kidney development in
zebrafish embryos by using a fluorescence dissecting microscope equipped for
structured illumination and z-stack acquisition. To visualize nephrogenesis,
transgenic zebrafish (Tg(wt1b:GFP)) with fluorescently labeled kidney structures
were used. Renal defects were triggered by injection of an antisense morpholino
oligonucleotide against the Wilms tumor gene wt1a, a factor known to be crucial
for kidney development. The advantage of the experimental setup is the
combination of a zoom microscope with simple strategies for re-adjusting
movements in x, y or z direction without additional equipment. To circumvent
focal drift that is induced by temperature variations and mechanical vibrations,
an autofocus strategy was applied instead of utilizing a usually required
environmental chamber. In order to re-adjust the positional changes due to a
xy-drift, imaging chambers with imprinted relocation grids were employed. In
comparison to more complex setups for time-lapse recording with optical
sectioning such as confocal laser scanning or light sheet microscopes, a zoom
microscope is easy to handle. Besides, it offers dissecting microscope-specific
benefits such as high depth of field and an extended working distance. The method
to study organogenesis presented here can also be used with fluorescence stereo
microscopes not capable of optical sectioning. Although limited for
high-throughput, this technique offers an alternative to more complex equipment
that is normally used for time-lapse recording of developing tissues and organ
dynamics.
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