4. Switch to a higher magnification objective. For oil-immersion
lenses only, remember to add a drop of oil on the lens surface.
5. Focus on the embryo using the eyepieces and bright field
illumination (see Note 18).
6. Use the live imaging mode in the image acquisition software
with the appropriate laser illumination to visualize fluorescent
proteins and focus on the correct plane (see Notes 19 and 20).
7. Adjust the laser power and exposure time for optimal imaging
(Fig. 3, see Notes 21 and 22).
8. To acquire a movie, select the range of depths in which images
must be acquired, the spacing of slices within that range, and
the temporal resolution of image acquisition (Movie 3, see
Note 23).
9. Depending on the microscope set up, multiple stage positions
can be acquired for imaging larger samples or multiple samples
in parallel.
3.4.2 Wound Healing
Assays
For image acquisition:
1. Focus the objective on the plane in which ablation should
occur.
2. Select the number of pulses to use for ablation (see Note 24).
Test the laser to make sure that it can sever the tissue of interest
(Movie 4, see Note 25).
3. Find a new embryo and acquire at least two-to-four images of
the cells that will be wounded to be able to reliably calculate
their area before wounding.
4. Fire the laser on one or more spots (see Note 26).
5. Continue imaging and monitoring the progress of wound
closure until the wound is closed (Movie 3).
For image analysis:
5. Using ImageJ (or other software) obtain maximum intensity
projections for each of the stacks by opening you image and
selecting “Image > Stacks > Z-project . . .”. Concatenate each
of the resulting images into a time-sequence of the wound
healing response by using “Image > Stacks > Z-project > Tools > Concatenate . . .”.
6. Segment the wound margin, for instance, using the semiautomated LiveWire algorithm in SIESTA [28] based on Dijkstra’s optimal path search [31] (under “Annotations > LiveWire”) (Fig. 4a). SIESTA also enables automated delineation
of the wound edge using active contours, a region growing
algorithm [32] (under Annotations > MEDUSA). Equivalent
segmentation algorithms can be found in other software tools.
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Gordana Scepanovic et al.
lenses only, remember to add a drop of oil on the lens surface.
5. Focus on the embryo using the eyepieces and bright field
illumination (see Note 18).
6. Use the live imaging mode in the image acquisition software
with the appropriate laser illumination to visualize fluorescent
proteins and focus on the correct plane (see Notes 19 and 20).
7. Adjust the laser power and exposure time for optimal imaging
(Fig. 3, see Notes 21 and 22).
8. To acquire a movie, select the range of depths in which images
must be acquired, the spacing of slices within that range, and
the temporal resolution of image acquisition (Movie 3, see
Note 23).
9. Depending on the microscope set up, multiple stage positions
can be acquired for imaging larger samples or multiple samples
in parallel.
3.4.2 Wound Healing
Assays
For image acquisition:
1. Focus the objective on the plane in which ablation should
occur.
2. Select the number of pulses to use for ablation (see Note 24).
Test the laser to make sure that it can sever the tissue of interest
(Movie 4, see Note 25).
3. Find a new embryo and acquire at least two-to-four images of
the cells that will be wounded to be able to reliably calculate
their area before wounding.
4. Fire the laser on one or more spots (see Note 26).
5. Continue imaging and monitoring the progress of wound
closure until the wound is closed (Movie 3).
For image analysis:
5. Using ImageJ (or other software) obtain maximum intensity
projections for each of the stacks by opening you image and
selecting “Image > Stacks > Z-project . . .”. Concatenate each
of the resulting images into a time-sequence of the wound
healing response by using “Image > Stacks > Z-project > Tools > Concatenate . . .”.
6. Segment the wound margin, for instance, using the semiautomated LiveWire algorithm in SIESTA [28] based on Dijkstra’s optimal path search [31] (under “Annotations > LiveWire”) (Fig. 4a). SIESTA also enables automated delineation
of the wound edge using active contours, a region growing
algorithm [32] (under Annotations > MEDUSA). Equivalent
segmentation algorithms can be found in other software tools.
210
Gordana Scepanovic et al.