ImageJ

1.    Load image into ImageJ

2.    Rotate so the the axon is coming up from the bottom

a. “image” “transform” rotate, or flip to produce mirror image

3.    “Process” “smooth” makes the neurons a little smoother looking

4.     Select a section mostly including the neuron in interest

5.    “Image” “duplicate” to duplicate the selected region (this step is important to keep your original)

6.    “Image” “type” “8-bit” (turns the image black and grey)

7.    “Edit” “invert” (image now becomes white and grey)

8.    “Image” “adjust” “brightness/contrast” to make neurons stand out more

9.    “Save” Note: save as .jpeg in order to see changes without opening in ImageJ

Class 1 neuron in segment A1 (Gal4^221, UAS-MCD8-GFP). Same image from previous page, transformed with ImageJ.

Isn’t it beautiful????

    

Arvind’s Pruning Analysis Protocol

Sample preparation

1.     Clear all pupae to start time course

2.     Check vial every hr to look for pupationàmark time when pupated and remove pupae from vial and place on glass slide with double sided tape in following orientation:

3.      [insert image here]

a. 90degree turns and ~75degree turns seem to place the class I ddaD/E cell body cluster near the center of the pupae—over turning can lead to dendritic projections/cell body to be cut of because they are “turning” around the edge and as a result difficult to image

b.      [insert diagram for what happens when overturning pupae]

c. Look for distinct ddaD morphology—primary dendrite will be pointing towards center of body(towards trachea) and secondary branches will point posteriorly innervating the denticles (often accompanied by ddaE with secondary dendrites pointing anteriorly but sometimes these two cells can be separated if pupae is moved around too much after mounting coverslip)

4.     Once correct neuron is located set end and start points for the z stack (end point should be the cell bodyàas you move deeper in to the body you should see the dendritic projections and set the start point once you pass all dendritic projections)

5.     Start assembling z stack

Analysis

1.     Open z stack in imagejàclick ok to bioformats pop up

2.     Scroll through the z stack to see if the cell body of the neuron is included in the z stack and visible—

a. If not then note this on data analysis—do not include in quant

b. If cell body is visible proceed with analysis

3.     Click imagesàStacksàz project

a. If a lot of noise/background gfp from pupal casing, you can remove some slices of the z stack at beginning or end of stack with this pop up window as long as you are not cutting off dendrite or cell body

4.     Convert z projection to 8bit(imageàtypeà8bit)

5.     Click editàinvert so that neuron is now black and background is white

6.     Transform image so that dendrites pointing up and axons pt down(this should be a 90deg left turn unless pupae was mounted in a way that deviated from standard mounting above—note this in data analysis)

7.     Look at black/white max projection image to see if there are any discontinuities in primary or secondary branches

a. if it appears like there is, return to the z stack and scroll through the stack to verify that this is actually the case. Occasionally this discontinuity is just be an artifact of the black/white conversion—look to see if the same region of the dendrites is severed when scrolling through the z stack (transform this z stack in the same orientation as the binary image if this helps locate the region of interest)

b. in the data analysis spreadsheet label this neuron as “primary yes/no(distal/proximal); secondary yes/no(distal/ proximal)” under the appropriate time pt

c. also note whether dendritic debris is cleared (ie is there severed branch

es lingering)—“yes cleared/no not cleared”

8.     save binary image as tiff file in folder labelled “[insert date here] pruning”

* things to note:

Images from same neuron at different time points should be analyzed in a separate folder(the first image can be used with the other quant data but to avoid counting severing from the same neuron from the same biological replicate “twice” and skewing data, analyze the subsequent images in a separate time course folder